diff --git a/CHANGELOG.md b/CHANGELOG.md index 732770b..bbae03f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,102 @@ # Changelog +## 0.11.0 (2026-10-23) + +Goal: close the gap between the 0.10.0 live agent and its strategy on the all-time peak (July 0.8822 against scripted v8 0.8491, August 0.8940 against 0.8570) and the daily peak, bring August ramping (1.0157, the one KPI over the baseline) down, and set up a clean holdout that nothing in this release is tuned on. The envelope does not change. No live model calls are made for this release. + +**The holdout** (`HOLDOUT.md`), chosen and sealed before any tuning: Buildings 4, 5 and 6 of `citylearn_challenge_2023_phase_3_1`, June, July and August. No dataset CityLearn v3.0.2 ships is both new and fully compatible; this is the closest, new in its buildings but on the tuning months' weather, tariff and carbon. `hyperbolic months` and `compare` refuse it without `--holdout-final`, and only the baseline was run on it. + +**Acceptance rule for prompt version 9, written before any v9 result.** On the mean of the four months of the month set (phase 2, tuning June, July and August), each from `hyperbolic months` at CityLearn's six decimals, a v9 scripted candidate passes when, against v8 scripted: + +- `all_time_peak_average` is lower and `ramping_average` is lower; +- `cost_total`, `carbon_emissions_total`, `discomfort_proportion` and `discomfort_hot_proportion` are each no more than 0.001 higher; +- `power_outage_normalized_unserved_energy_total` is not higher at all. + +Among passing candidates the one with the lowest mean all-time peak plus mean ramping ships, ties going to the lower mean cost. If no candidate passes, nothing new ships: v8 stays the default strategy and this entry says so. The holdout plays no part. + +**Where the 0.10.0 live peaks came from** (`scripts/analyze_peaks.py` on the four live months; every replay runs the month's own replies through the unchanged envelope on the month it was recorded on, and every control replay reproduces the published KPIs exactly). The all-time peak is one hour, the district's highest net draw of the month: + +| Month | Haiku's all-time peak | Scripted v8's | What Haiku sent there | +|---|---|---|---| +| phase 2 | step 669 (day 28, 10 pm), 18.96 kWh (Building_1 8.00, Building_2 1.21, Building_3 9.75) | the same hour, 18.96 | the strategy's actions in that hour and the three before | +| June | step 669 (day 28, 10 pm), 15.10 | the same hour, 15.08 | the strategy's actions | +| July | step 739 (day 31, 8 pm), 12.98 (Building_3 10.83) | the same hour, 12.49 | battery 0 in all three buildings where the strategy shaved (-0.033, -0.033, -0.164) | +| August | step 450 (day 19, 7 pm), 13.80 (Building_1 9.29) | the same hour, 13.23 | an all-null reply where the strategy shaved (-0.033, -0.033, -0.219) and held two trims | + +Each departure class put back to the strategy on its own (all-time peak, daily peak, ramping): + +| Replay | phase 2 | June | July | August | +|---|---|---|---|---| +| Haiku live (control) | 0.9590, 0.9074, 0.9969 | 0.8549, 0.8919, 0.9802 | 0.8822, 0.8943, 0.9744 | 0.8940, 0.8896, 1.0157 | +| Scripted v8 | 0.9588, 0.9095, 0.9983 | 0.8541, 0.8830, 0.9732 | 0.8491, 0.8854, 0.9677 | 0.8570, 0.8861, 1.0100 | +| Skipped shaves put back | 0.9590, 0.9078, 0.9907 | 0.8549, 0.8871, 0.9771 | 0.8637, 0.8882, 0.9701 | 0.8570, 0.8881, 1.0138 | +| Missed trim starts put back | 0.9590, 0.9067, 1.0033 | 0.8549, 0.8858, 0.9812 | 0.8822, 0.8930, 0.9811 | 0.8940, 0.8851, 1.0229 | +| Skipped charges put back | 0.9590, 0.9074, 0.9952 | 0.8549, 0.8887, 0.9651 | 0.8822, 0.8945, 0.9693 | 0.8940, 0.8899, 1.0066 | +| Every departure put back | 0.9591, 0.9080, 0.9988 | 0.8549, 0.8842, 0.9632 | 0.8621, 0.8857, 0.9700 | 0.8569, 0.8849, 1.0156 | + +Ranked by KPI points explained (live minus replay), summed over the four months: + +| Class (building-hours over four months) | All-time peak | Daily peak | Ramping | +|---|---|---|---| +| `shave_skipped`: skipped or cut a peak-shaving discharge (35) | +0.0555 | +0.0120 | +0.0155 | +| `trim_shallower` (82) | +0.0006 | +0.0003 | -0.0010 | +| `trim_not_started`: no trim in an overcooled room where the strategy trims (416) | 0.0000 | +0.0126 | -0.0212 | +| `charge_skipped`: charged less than the strategy in a charging hour (82) | 0.0000 | +0.0026 | +0.0311 | +| `trim_deeper` (30) | 0.0000 | +0.0018 | -0.0014 | +| `top_discharge_short` (17) | -0.0001 | -0.0030 | -0.0040 | +| every other class | 0.0000 | at most 0.0010 either way | at most 0.0031 either way | + +- **The all-time peak gap is skipped shaves.** Putting back 35 building-hours of skipped shaves (17 hours, a mean gap of 0.09 to 0.18 by month) takes August from 0.8940 to the strategy's 0.8570 and July from 0.8822 to 0.8637. 29 of the 35 came after the day's top-price hours and 8 of the 17 hours fell on a flat-price day. In 16 of the 17 the agent's reason says it followed the strategy while calling the batteries idle ("batteries idle-ish per flat price with peak limiter", "battery near idle as SoC low"). The version 8 prompt allowed that reading twice: "Before them discharge only to shave" and "Idle on flat_24h days", with no word that the strategy shaves after the top price and on flat days. +- **The daily peak gap** is the missed trim starts (+0.0126) and the skipped shaves (+0.0120). 415 of the 416 missed trim starts are `null` sent while the trim state was off (median 0.043 above the strategy's trim), the 0.9.0 habit version 8's wording did not end. +- **Ramping:** skipped charges add 0.0311 over the four months; the missed trim starts take 0.0212 off. +- `scripts/analyze_deviations.py` now reads version 8 runs: the strategy is the one the observation shows, replays run on the run's own month, and one class is new, `warm_skipped`. + +**August ramping, split** (`scripts/split_ramping.py`, now on any month of the month set; points of `ramping_average` above 1.0, against a scripted v8 August recorded offline): + +| Cause | Haiku v8 live (1.0157) | Scripted v8 (1.0100) | Agent minus strategy | +|---|---|---|---| +| Battery charging | +2.24 | +1.44 | +0.80 | +| Battery discharging | -1.13 | -1.58 | +0.45 | +| Trim start, end, depth | -2.95, +3.47, -0.09 | -2.40, +3.91, -0.39 | -0.69 | +| Extra cooling | +0.02 | +0.02 | 0.00 | +| Total | +1.57 | +1.00 | +0.57 | + +1.00 of the 1.57 points is the strategy itself, mostly its trim ends (+3.91: the envelope ends a trim at once at the setpoint or when the room empties, and eases it by at most 0.02 an hour). The agent adds 0.57: 1.25 from its battery (uneven charging, skipped shaves) less 0.69 from the trims it did not start. + +**The search** (`scripts/tune_strategy.py --version 9`, four months of the month set, scripted, the rule above applied as written). The diagnosis puts every all-time peak in an evening hour just after the top-price discharge, so the grid kept charge back through the top-price hours for that shave (`evening_reserve` 0 to 0.25), with the shave share (0.9, 0.8) and the grid charge rise (`charge_step` 0.15, 0.1, 0.05): + +- **First pass, 36 settings: none passed.** The reserve works (0.05: all-time peak 0.8643 against 0.8797, ramping 0.9788 against 0.9873) but costs about 0.003 per 0.05 (0.9117 against 0.9087). A slower charge rise lowers ramping but raises cost and the all-time peak; a lower shave share raises cost. +- **Second pass, added after the first and reported here as such: 8 settings with smaller reserves** (0.01 to 0.04, `charge_step` 0.15 or 0.125). One passed: `evening_reserve` 0.01, everything else at version 8. On the four-month mean: all-time peak 0.876779 against 0.879746, ramping 0.985732 against 0.987321, cost +0.000596, emissions +0.000024, discomfort, too warm and outage energy unchanged to six decimals. It is lower on the all-time peak in every month and on ramping in three of four (July +0.0004). The same numbers come out of `hyperbolic months` with the shipped prompt version 9. +- How it works, at tuning July's peak hour: each battery comes out of the top-price hours with 0.009 more charge (0.243 against 0.234), the shave discharges it (-0.041 against -0.033), and the peak falls from 12.49 to 12.42 kWh. + +Changes: + +- **Prompt version 9 (default).** The strategy is version 8's with `evening_reserve` 0.01 (`StrategyParamsV9`, `strategy_plan_v9`). The observation adds `peak.shave_now`, the strategy's own test for a peak hour. The system prompt says to send every number exactly ("battery -0.033 is not 0, and cooling under baseline_action is not null, trim state off too"), to shave in any hour `shave_now` is true, after the top price and on flat_24h days too, with whatever charge is left, and to keep 0.01 above the floor in the top-price hours; "Before them discharge only to shave" and "Idle on flat_24h days" are gone. Version 8 stays selectable and its requests are unchanged byte for byte. +- **The holdout and its guard**: `HOLDOUT.md`, `holdout/citylearn_challenge_2023_phase_3_1` (copied unchanged), `hyperbolic months --holdout-final`, `hyperbolic compare --holdout-final`, a seal of file hashes checked before any scoring, `run --month holdout-06` (07, 08) recording without printing KPIs, and `months()` never listing it. +- **Tools.** `scripts/analyze_peaks.py` (peak hours, the model against its strategy there, class replays, the ranking). `scripts/analyze_deviations.py` reads version 8 runs and keeps each replay's hourly grid draw. `scripts/split_ramping.py` resimulates tuning months. `scripts/tune_strategy.py --version 9` with `accepts_v9`. +- `hyperbolic months` runs `v9` scripted too. `hyperbolic serve`'s first cost estimate uses the version 9 request size (4,810 input and 220 output tokens). +- Tests cover the holdout (its months, the schema with only its buildings, the seal and a broken seal, the guard in `months`, `compare`, `run_month` and recorded runs, `run` printing no KPIs, the baseline loading), the peak hours and ranking, version 8 deviation classes and `warm_skipped`, replays on their own month and never the holdout, a CityLearn run that never shaves, the acceptance rule case by case and the pick, the version 9 grid, the version 9 observation, strategy, wording, request size and scripted reply, version 9 runs that replay on the fake district and on 24 hours of CityLearn, and the four 0.10.0 live months replaying hour for hour when they are on disk. + +**Estimates on the four months, no holdout** (scripted rows are not model results; the last two rows send the 0.10.0 live replies again with one or every class put back): + +| Mean of four months | cost | emissions | discomfort | too warm | ramping | daily peak | all-time peak | load factor | outage | +|---|---|---|---|---|---|---|---|---|---| +| 0.4.0 rule-based | 0.9619 | 1.0002 | 0.1352 | 0.0188 | 1.0955 | 0.9847 | 0.9846 | 0.9851 | 0.1711 | +| v8 scripted | 0.9087 | 0.9582 | 0.0208 | 0.0059 | 0.9873 | 0.8910 | 0.8797 | 0.9549 | 0.1711 | +| **v9 scripted** | **0.9093** | **0.9582** | **0.0208** | **0.0059** | **0.9857** | **0.8898** | **0.8768** | **0.9540** | **0.1711** | +| Haiku v8 live (0.10.0) | 0.9122 | 0.9609 | 0.0241 | 0.0066 | 0.9918 | 0.8958 | 0.8975 | 0.9557 | 0.1711 | +| Haiku v8, skipped shaves put back | 0.9123 | 0.9608 | 0.0241 | 0.0066 | 0.9879 | 0.8928 | 0.8836 | 0.9535 | 0.1711 | +| Haiku v8, every departure put back | 0.9076 | 0.9567 | 0.0198 | 0.0057 | 0.9869 | 0.8907 | 0.8833 | 0.9551 | 0.1711 | + +By month, v9 scripted: cost 0.9037, 0.9105, 0.9199, 0.9029; all-time peak 0.9586, 0.8516, 0.8445, 0.8524; ramping 0.9952, 0.9717, 0.9681, 1.0080 (August still above the baseline); clamped hours 22, 32, 10, 3, as v8. + +- If Haiku shaves after the top price under the new wording, the July and August all-time peaks should come down to near the strategy's (estimated 0.8637 and 0.8570 from the shaves alone with the version 8 strategy). Only a live run can show whether it does. +- The v9 strategy's own gain is small: 0.003 on the all-time peak and 0.0016 on ramping for 0.0006 of cost. August ramping stays above 1.0 because of the trim ends the envelope fixes; this release does not change the envelope. + +**Request size and cost.** Over the same scripted phase 2 month a version 9 request averages 9,716 characters against 9,456 for version 8, 2.8 percent larger: the system prompt is 3,886 characters against 3,645 and the observation 18 longer; the reply schema is unchanged. Scaled from the 0.10.0 live months (4,676 input and 215 output tokens per call), a Haiku month should cost about $0.42 for 719 hours and $0.44 for 743 ($0.44 and $0.46 if replies run 25 percent longer); the hard worst case is $0.96 and $0.99, under the $2.00 cap. All four months should cost about $1.72 to $1.80, and the three holdout months about $1.30 to $1.36. + +The envelope is unchanged (`hyperbolic/envelope.py` is byte for byte 0.10.0's and `EnvelopeConfig` is still pinned to 0.8.0 by its test). The four 0.10.0 live months, the 0.9.0 and the 0.8.0 live months replay every hour (719, 719, 743, 743, 719, 719) with 0 API calls. No live model calls were made for this release, and nothing but the baseline has been run on the holdout. + ## 0.10.0 (2026-10-23) Goal: find and close the gap between the 0.9.0 live agent and its own strategy, fix the hot-side comfort miss, and score everything on more than one month. The envelope does not change. The analysis, strategy and estimates below were built before any live call; the live months on all four months of the set follow them. diff --git a/HOLDOUT.md b/HOLDOUT.md new file mode 100644 index 0000000..4ebba54 --- /dev/null +++ b/HOLDOUT.md @@ -0,0 +1,77 @@ +# Holdout + +Chosen and sealed on 2026-10-23, at the start of 0.11.0, before any 0.11.0 tuning and before any controller was run on it. Nothing in 0.11.0 or later is tuned on it, and it is scored once, for the final result, with `--holdout-final`. + +## The choice + +**Buildings 4, 5 and 6 of `citylearn_challenge_2023_phase_3_1`** (CityLearn v3.0.2), June, July and August, each its own episode: + +| Key | Dataset name in run manifests | Hours | +|---|---|---| +| `holdout-06` | `citylearn_challenge_2023_phase_3_1_buildings_4_5_6_month_06` | 719 | +| `holdout-07` | `citylearn_challenge_2023_phase_3_1_buildings_4_5_6_month_07` | 743 | +| `holdout-08` | `citylearn_challenge_2023_phase_3_1_buildings_4_5_6_month_08` | 743 | + +The files are copied unchanged into `holdout/citylearn_challenge_2023_phase_3_1` (all 16, Buildings 1 to 3 included, so the copy can be checked against upstream file for file). A holdout month is written out with only Buildings 4, 5 and 6 in its schema. + +## No fully clean holdout exists + +Plainly: CityLearn v3.0.2 ships no dataset that is both new to this project and fully compatible with it. The closest option is the one above, and it is new only in its buildings. + +Every dataset CityLearn v3.0.2 lists (`DataSet.get_dataset_names()`; the package fetches them from the CityLearn repository at that tag), checked against the project's three actions (`cooling_device`, `dhw_storage`, `electrical_storage`) and 30 active observations: + +| Datasets | Actions | Why not | +|---|---|---| +| `citylearn_challenge_2023_phase_2_local_evaluation` | same | the reported phase 2 month | +| `citylearn_challenge_2023_phase_1` | same | byte for byte the phase 2 month's data | +| `citylearn_challenge_2023_phase_2_online_evaluation_1` | same | the tuning months | +| `..._online_evaluation_2`, `..._online_evaluation_3` | same | the tuning months with another outage seed | +| `citylearn_challenge_2023_phase_3_1`, `_3_2`, `_3_3` | same | Buildings 1 to 3, weather, pricing and carbon are value for value the tuning months (all 2,208 hours, every column); Buildings 4 to 6 are new. The three sets differ only in the outage seed (65647, 42647, 57841) | +| `citylearn_challenge_2020_climate_zone_1` to `4`, `citylearn_challenge_2021` | cooling storage, dhw, battery | no heat pump action, no LSTM building model, other observations | +| `citylearn_challenge_2022_phase_1` to `3`, `_phase_all` and its variants | battery only (some with EVs, deferrable loads) | no cooling or tank action | +| `baeda_3dem` | cooling, cooling storage, dhw | no battery, other observations | +| `ca_alameda_county_neighborhood`, `tx_travis_county_neighborhood`, `vt_chittenden_county_neighborhood` | `cooling_or_heating_device`, dhw, battery | a combined heating and cooling action, 9 of the 30 observations missing and 16 others added, 47 to 100 buildings, a full year | +| `quebec_neighborhood_with_demand_response_set_points`, `..._without_...` | heating only | no cooling, battery or tank action | +| `citylearn_three_phase_*` demos | EVs, battery, deferrable loads | no cooling or tank action | + +## Why Buildings 4, 5 and 6 of phase 3 set 1 + +- **Compatible.** The same three actions and the same 30 observations as the project's month set. Each building is a `citylearn.building.LSTMDynamicsBuilding` with a heat pump for cooling, an electric heater with a hot water tank, a battery and 2.8 kW of PV, and CityLearn's stochastic outage model. The project's code and prompts name no building. A baseline episode of each month loads and runs (checked in 0.11.0; no other controller was run). +- **New to the project.** Their energy simulation data (loads, cooling and hot water demand, occupancy, setpoints, the no-control indoor temperature) and their LSTM models (`Building_4.pth` to `Building_6.pth`) appear in no dataset this project has run, tuned or reported on. Their batteries and heat pumps are their own sizes (Building_4: battery 3.3 kWh at 1.61 kW, heat pump 3.6 kW; Building_5: 4.0 kWh at 3.32 kW, 5.8 kW; Building_6: 3.3 kWh at 1.61 kW, 4.1 kW). Their occupied-hour cooling setpoints run from 21.7 to 27.2 C, a range like the project's buildings'. +- **Not new: the weather, the tariff and the carbon intensity.** They are the tuning months' series, hour for hour, so the strategy settings searched on the tuning months have seen these prices and this weather. The holdout tests the agent and the strategy on buildings they were not shaped on, not on an unseen climate or tariff. +- **Outages.** The stochastic outage model is on for all three buildings (seed 65647, SAIFI 1.436, CAIDI 331.2), so outage hours fall where the project has not seen them. The tuning months have no outages; phase 2 has one, of 15 hours. +- **Set 1, not 2 or 3.** They differ only in the outage seed. Set 1 was picked first, before anything was run on any of them. +- **Three months, each its own episode**, the same split as the tuning months, so a result does not rest on one month. + +The closest alternatives were weaker. All six buildings of phase 3 would mix three buildings the project has tuned on. Online evaluation 2 or 3 is the tuning months again with only the outage seed changed. The neighbourhood datasets would need a different action and observation mapping, which would test the adapter more than the controller. + +## The seal + +- `hyperbolic months` and `hyperbolic compare` refuse a holdout month or a run recorded on one, for any controller, unless `--holdout-final` is passed. With it, `months` scores the holdout months only, never mixed with the month set. +- The viewer data writer (`compare` and the live page's Publish) refuses a holdout pair without the flag. +- `hyperbolic run --month holdout-06` (and 07, 08) records a run but prints no KPIs. `hyperbolic baseline` runs on the holdout, as the load check needs. +- `hyperbolic.months.months()` never lists the holdout, so `scripts/tune_strategy.py`, `scripts/analyze_deviations.py` and the default `hyperbolic months` cannot reach it. +- Before scoring, the code checks every file in `holdout/citylearn_challenge_2023_phase_3_1` against these sha256 hashes (text files with LF line endings) and refuses if any differs or an extra file appears: + +| File | sha256 | +|---|---| +| Building_1.csv | a7b27f4a3622305e60da0a32a92d468563705f69ebdec2c13d0dc6b2fd705ac1 | +| Building_1.pth | 15e137b9e1930370708346d1298ca1f8b937cdb7c8e47e43f2555e543528d394 | +| Building_2.csv | 2993728aaf8e270abaa90f08a7aec4e66f584f58927ce6d6dfe5d9582929e109 | +| Building_2.pth | 36515f53e995061148697327ee416ed305c756418584f430597616399342a0a3 | +| Building_3.csv | aca204f9943850e794021450a23ed97407a9fed07fc43c6b14123e6c7667ab0a | +| Building_3.pth | 42d10bf54ea210d0626d1d55568f0ad7a0b9fb600f5e2802420a3a4d77168794 | +| Building_4.csv | 89d94801d834ce1409ca360b661357176a73834a9f5a90b2345528ba320bd956 | +| Building_4.pth | 4eabbb6c24e2c0482c55768541dce6c1f75e5aedcbaf01ae5c8786177a96d974 | +| Building_5.csv | 9c5a9a91dd4cb22ffa96c0dad75a995f33a6cda95cab1fd3dfafbf5e2e1da672 | +| Building_5.pth | f0e39abe58629e28d9cbaf8ffdadf41163aa97a0ec5763608d3028c1e47b22ae | +| Building_6.csv | ecc75e8eb17a4b9e6d28e90cfc8892a596b18197e9d84bbb492b1c050b93bf71 | +| Building_6.pth | d349f9cf27c6ebad5a226c81e7729c0050bdb974793acfbd1cfbf1ff600ee2b6 | +| carbon_intensity.csv | f106216393efe4eee30a5b85823f1fddff2676a09a58a3f63cd28482220516ad | +| pricing.csv | f06036bdb7b72d4bf13547da6f8453ab9f7095c5fabcf2b6929f93a79e1d0126 | +| schema.json | 500bdd747f45d8b63295a5ce7ee557ffda930d25210c32c591ef15d23e3306af | +| weather.csv | aeb1420f6102c5df2bddc55f211d41cf16e5f572a6fcce3cce8ad37b007875ce | + +The seal is a procedure, not cryptography: a recorded run's `result.json` still holds its KPIs. What it guarantees is that the project's own tools never score the holdout by accident, and that the files scored at the end are the files chosen here. + +The data is copied unchanged from the CityLearn repository (tag v3.0.2) under the MIT license in `hyperbolic/data/CITYLEARN_LICENSE.txt`. diff --git a/README.md b/README.md index 4dd4015..cd6b7be 100644 --- a/README.md +++ b/README.md @@ -564,9 +564,54 @@ Thermal resilience is scored only where there are outages: on the phase 2 month **The published viewer now shows the 0.10.0 phase 2 live run** in place of 0.9.0 (the page's headline: cost down 9.6 percent, emissions down 5.5 percent at equal or better comfort). The phase 2 month stays the published one so the page stays comparable with earlier releases. +### Version 0.11.0 + +**Goal:** close the two gaps the 0.10.0 live months left, the all-time peak (July 0.8822 against scripted v8 0.8491, August 0.8940 against 0.8570, with the daily peak about 0.009 behind too) and August ramping (1.0157, the one KPI over the baseline), and set up a holdout nothing in this release is tuned on. The envelope is identical to 0.10.0. No live model calls were made: everything here is measured on recorded runs and offline controllers, and the published viewer still shows the 0.10.0 phase 2 run. + +**A holdout, chosen and sealed first** ([HOLDOUT.md](HOLDOUT.md)). Every result so far is on data the project has looked at: phase 2 shaped the outage exception and the tuning months chose the strategy's numbers. CityLearn v3.0.2 ships no dataset that is both new to the project and fully compatible with it. Every 2023 set reuses the phase 2 or tuning data, and every other dataset has other actions or observations. The closest, chosen before any tuning, is **Buildings 4, 5 and 6 of `citylearn_challenge_2023_phase_3_1`**, June, July and August (`holdout-06`, `holdout-07`, `holdout-08`). It has the same actions and observations, new loads, occupancy, LSTM models, batteries and heat pumps, and stochastic outages. Its weather, tariff and carbon intensity are the tuning months', hour for hour, so it tests new buildings, not a new climate. `hyperbolic months` and `compare` refuse it without `--holdout-final`, the files are checked against a seal of hashes before scoring, and only the baseline has been run on it. + +**The acceptance rule was written into the CHANGELOG before any version 9 result:** on the mean of the four months, v9 scripted must have a lower all-time peak and lower ramping than v8 scripted, no more than 0.001 worse cost, emissions, discomfort or too warm, and no worse outage energy; otherwise nothing new ships. + +**Where the live peaks came from** (`scripts/analyze_peaks.py`). The all-time peak is a single hour. In phase 2 and June Haiku sent exactly the strategy's actions in it and the hours before. In July (day 31, 8 pm) it sent battery 0 where the strategy shaved, and in August (day 19, 7 pm) an all-null reply. Each departure class put back to the strategy on its own, through the unchanged envelope (control replays reproduce the published KPIs exactly): + +| KPI points explained, sum of four months | All-time peak | Daily peak | Ramping | +|---|---|---|---| +| Skipped shaves (35 building-hours) | +0.0555 | +0.0120 | +0.0155 | +| Missed trim starts (416) | 0.0000 | +0.0126 | -0.0212 | +| Skipped charges (82) | 0.0000 | +0.0026 | +0.0311 | +| Every other class | at most 0.0006 | at most 0.0030 either way | at most 0.0040 either way | + +- **Skipped shaves are the all-time peak gap.** Putting them back takes August from 0.8940 to the strategy's 0.8570 and July from 0.8822 to 0.8637. 29 of the 35 came after the top-price hours and 8 of the 17 hours fell on flat-price days. The agent's reasons say it followed the strategy while calling the batteries idle; the version 8 prompt said "Before them discharge only to shave" and "Idle on flat_24h days". +- **Missed trim starts** (415 of 416 are `null` with the trim state off) and skipped shaves make the daily peak gap. Skipped charges are most of the agent's ramping. + +**August ramping, split exactly** (points above 1.0): scripted v8 +1.00 (battery charging +1.44, discharging -1.58, trims +1.12, of which trim ends +3.91), Haiku +1.57 (battery +1.11, trims +0.43). The strategy is 1.00 of the 1.57; the agent adds 0.57, its battery +1.25 less 0.69 from the trims it did not start. Most of the strategy's own share is trim ends, which the envelope fixes (a trim ends at once at the setpoint and eases by at most 0.02 an hour), so a strategy change cannot remove it. + +**The search** (`scripts/tune_strategy.py --version 9`, the four months, the rule as written). Every all-time peak falls in an evening hour just after the top-price discharge, so the grid kept charge back through the top-price hours for that shave (`evening_reserve`), with the shave share and the grid charge rise. **The first pass, 36 settings, passed none**: a reserve of 0.05 cuts the all-time peak to 0.8643 and ramping to 0.9788 but costs 0.003. **A second, finer pass, added after that and reported as such, passed one of 8: `evening_reserve` 0.01.** It ships as the version 9 strategy. At tuning July's peak each battery leaves the top-price hours with 0.009 more charge, the shave discharges it, and the peak falls from 12.49 to 12.42 kWh. + +**Estimates, four months, no holdout** (`hyperbolic months`; scripted rows are not model results): + +| Mean of four months | cost | emissions | discomfort | too warm | ramping | daily peak | all-time peak | load factor | outage | +|---|---|---|---|---|---|---|---|---|---| +| 0.4.0 rule-based | 0.9619 | 1.0002 | 0.1352 | 0.0188 | 1.0955 | 0.9847 | 0.9846 | 0.9851 | 0.1711 | +| v8 scripted | 0.9087 | 0.9582 | 0.0208 | 0.0059 | 0.9873 | 0.8910 | 0.8797 | 0.9549 | 0.1711 | +| **v9 scripted** | **0.9093** | **0.9582** | **0.0208** | **0.0059** | **0.9857** | **0.8898** | **0.8768** | **0.9540** | **0.1711** | +| Haiku v8 live (0.10.0) | 0.9122 | 0.9609 | 0.0241 | 0.0066 | 0.9918 | 0.8958 | 0.8975 | 0.9557 | 0.1711 | +| Haiku v8, skipped shaves put back | 0.9123 | 0.9608 | 0.0241 | 0.0066 | 0.9879 | 0.8928 | 0.8836 | 0.9535 | 0.1711 | + +- **v9 passes the acceptance rule**, narrowly: all-time peak 0.876779 against 0.879746, ramping 0.985732 against 0.987321, cost +0.000596, emissions +0.000024, discomfort, too warm and outage energy unchanged to six decimals. It is lower on the all-time peak in every month and on ramping in three of four (July +0.0004); August ramping is 1.0080, still above the baseline. +- **The bigger lever is the agent.** Putting back only the skipped shaves would take the 0.10.0 live mean all-time peak from 0.8975 to 0.8836. Prompt version 9's wording targets exactly those hours. Only live runs can show whether Haiku now shaves after the top price and starts trims. + +**Request size and cost.** A version 9 request averages 9,716 characters against 9,456 for version 8 (2.8 percent larger; system prompt +241, observation +18). A Haiku month should cost about $0.42 (719 hours) to $0.44 (743 hours), with a hard worst case of $0.96 to $0.99 under the $2.00 cap: about $1.72 to $1.80 for the four months and $1.30 to $1.36 for the three holdout months. + ## What the agent sees -Prompt version 8 (`hyperbolic/prompt.py`) is the default from 0.10.0. It keeps everything in version 7 and: +Prompt version 9 (`hyperbolic/prompt.py`) is the default from 0.11.0. It keeps everything in version 8 and: + +- **Says to send every number of `strategy` exactly**: a battery -0.033 is not 0, and cooling under baseline_action is not `null`, with the trim state off too. The 0.10.0 live months left the batteries idle in skipped shaves and sent `null` in 415 of 416 missed trim starts. +- **Shows `peak.shave_now`**, the strategy's own test for a peak hour, and says to shave whenever it is true, after the top price and on flat_24h days too, with whatever charge is left. Version 8's "Before them discharge only to shave" and "Idle on flat_24h days" are gone. +- **Keeps 0.01 of charge above the floor through the top-price hours** (`StrategyParamsV9.evening_reserve`), so the evening shave has something to discharge. + +Prompt version 8, the default in 0.10.0, keeps everything in version 7 and: - **Shows the strategy's whole action per building**, `strategy` with `cooling`, `dhw` and `battery` (from `strategy_plan_v8`, inside the envelope's ranges), and `strategy_reason`, in place of `strategy_battery` and `strategy_battery_reason`. The agent is told to send it as given and to say why where it differs. - **Says a trim starts when an overcooled room's trim state is off**, and that a held trim is a number inside `cooling_range`, never `null`. The version 7 line that `null` is for "only when trim state is off or end" is gone: the 0.9.0 live month read it as "send null when the trim is off". @@ -610,12 +655,14 @@ Prompt version 3, the default in 0.4.0, keeps everything in version 2 and adds t - **Storage per building:** state of charge, capacity and how far the battery can move in one hour, for the battery and the hot water tank. - **A plain strategy:** charge the battery in the cheapest, cleanest hours and with spare solar, discharge into the price peak sized to the building's own load (exports earn nothing), mostly leave the hot water tank alone, keep cooling at the baseline unless a small pre-cool is clearly safe, and hold when nothing stands out. -Nothing reads the dataset ahead of time; the outlook is built only from observations the controller has already received. Prompt versions 1 to 7 are kept unchanged so runs recorded with 0.2.0 to 0.9.0 replay byte for byte, and `--prompt-version 1` to `7` still selects them: 1 to 5 with the 0.7.0 envelope rules, 6 and 7 with the 0.8.0 rules that version 8 also uses. +Nothing reads the dataset ahead of time; the outlook is built only from observations the controller has already received. Prompt versions 1 to 8 are kept unchanged so runs recorded with 0.2.0 to 0.10.0 replay byte for byte, and `--prompt-version 1` to `8` still selects them: 1 to 5 with the 0.7.0 envelope rules, 6 to 8 with the 0.8.0 rules that version 9 also uses. ## Tuning without peeking at the test month From 0.6.0 there is a separate 2023 tuning set. `scripts/tuning_data` holds `citylearn_challenge_2023_phase_2_online_evaluation_1` from CityLearn v3.0.2, copied unchanged: the same three buildings, batteries and LSTM models as the reported month, but June, July and August of a different period, with its own weather, loads, solar, carbon intensity and tariff, and no hour in common with the reported month. (`citylearn_challenge_2023_phase_1` is byte for byte the reported month's data, so it is no use for tuning; online evaluation sets 2 and 3 differ from set 1 only in the outage seed.) +In 0.11.0, `python scripts/tune_strategy.py --version 9` searched charge kept for the evening shave (`StrategyParamsV9.evening_reserve`), the shave share and the grid charge rise on the four months of the month set, the phase 2 month included, under the acceptance rule written into the CHANGELOG first (see [Version 0.11.0](#version-0110)). The first pass of 36 settings passed none and a finer second pass of 8 passed one. The holdout ([HOLDOUT.md](HOLDOUT.md), Buildings 4 to 6 of `citylearn_challenge_2023_phase_3_1`) was sealed before the search and played no part: `months()` never lists it, and scoring it needs `--holdout-final`. + In 0.10.0, `python scripts/tune_strategy.py --version 8` searched the version 8 strategy's extra cooling in a warm room (`StrategyParamsV8`) and a no-trim night, 36 settings with the 0.8.0 rules, with comfort first: too warm at or below the baseline on every tuning month, too cool within 0.001 of version 7's, ramping and both peaks at or below the baseline's, then the lowest cost plus emissions (see [Version 0.10.0](#version-0100)). The tuning months are now also part of the month set (`hyperbolic.months`, `hyperbolic months`), which the search and the compare report share; the phase 2 month is still only reported, never searched. In 0.9.0, `python scripts/tune_strategy.py --version 7` searched the version 7 battery plan's smoothing (`StrategyParamsV7`): a soft cap on how fast a battery action may rise (0.05 to 0.25, on charging and discharging or on a discharge winding down only), valley filling when grid charging, and a charge rate of 0.1 or 0.15, 36 settings with the 0.8.0 rules. Every candidate runs the same cooling, so the reference is the candidate with version 6's battery plan, and a setting qualified when ramping was lower with cost, emissions, discomfort and both peaks no worse. None did. Every cap raised cost and the all-time peak (a cap of 0.1: cost 0.9138 against 0.9086, all-time peak 0.8835 against 0.8526, ramping 0.9965 against 0.9823), and capping only the wind-down of a discharge raised ramping (1.0016). Charging at 0.1 cut ramping to 0.9644 and valley filling to as low as 0.9116, but both raised cost and the all-time peak. The shipped numbers are version 5's. @@ -657,7 +704,7 @@ The baseline action under active control is storage idle and the heat pump at th - Key from `HYPERBOLIC_ANTHROPIC_KEY`, falling back to `TRIAGE_ANTHROPIC_KEY`. The key lives only in the environment, never in a file, log or run folder (a test checks this). - Model: `--model haiku` (default, `claude-haiku-5-5`) or `--model sonnet` (`claude-sonnet-5-5`). Structured JSON output, low effort, 2000 max tokens. - Hard budget per run, `--budget` (default 2.00 USD). Before every call the runner computes that call's worst case (prompt size plus full max tokens). If it could push spending past the budget, model calls stop for the rest of the run and every remaining hour uses the baseline action. -- Episode length: the full 719 hours. The 0.2.0 live run averaged about 4,700 characters per request and cost 0.23 USD on Haiku. Prompt version 2 adds the 24 hour outlook and the strategy, about 7,400 characters per request, and the 0.3.0 live Haiku month cost 0.39 USD. Prompt version 3 is about 8,600 characters per request; the 0.4.0 live Haiku month cost 0.47 USD, against a hard worst case of about 0.93 USD. Prompt version 4 adds the reference action, the price plan and the battery limits, about 9,650 characters per request: the 0.6.0 live Haiku month cost 0.57 USD, against a hard worst case of about 0.95 USD. Prompt version 5 adds the smoothing view, about 10,430 characters per request: the 0.7.0 live Haiku month cost 0.63 USD, against a hard worst case of about 0.97 USD and the default 2.00 USD budget. Prompt version 6 adds the trim hold and `end_trim`, about 10,930 characters per request (4.8 percent more): the 0.8.0 live Haiku month cost 0.53 USD (5,245 input and 429 output tokens per call), against a hard worst case of about 0.99 USD. Prompt version 7 shows the envelope's ranges and is 1.7 percent smaller, about 10,750 characters: the 0.9.0 live Haiku month cost 0.46 USD (5,185 input and 245 output tokens per call). Prompt version 8 shows the strategy's whole action and is 12.0 percent smaller again, about 9,460 characters: the 0.10.0 live Haiku months cost 0.41 to 0.43 USD, against a hard worst case of about 0.95 USD (0.98 USD for a 743 hour tuning month). Sonnet costs 20 times as much per token: a full Sonnet month should cost roughly 11 to 13 USD, so under a 2.00 USD budget it stops calling the model after about a sixth of the month and the remaining hours use the baseline action. Raise `--budget` (about 14 USD) for a full Sonnet run. Each run logs its exact token cost in `result.json`. +- Episode length: the full 719 hours. The 0.2.0 live run averaged about 4,700 characters per request and cost 0.23 USD on Haiku. Prompt version 2 adds the 24 hour outlook and the strategy, about 7,400 characters per request, and the 0.3.0 live Haiku month cost 0.39 USD. Prompt version 3 is about 8,600 characters per request; the 0.4.0 live Haiku month cost 0.47 USD, against a hard worst case of about 0.93 USD. Prompt version 4 adds the reference action, the price plan and the battery limits, about 9,650 characters per request: the 0.6.0 live Haiku month cost 0.57 USD, against a hard worst case of about 0.95 USD. Prompt version 5 adds the smoothing view, about 10,430 characters per request: the 0.7.0 live Haiku month cost 0.63 USD, against a hard worst case of about 0.97 USD and the default 2.00 USD budget. Prompt version 6 adds the trim hold and `end_trim`, about 10,930 characters per request (4.8 percent more): the 0.8.0 live Haiku month cost 0.53 USD (5,245 input and 429 output tokens per call), against a hard worst case of about 0.99 USD. Prompt version 7 shows the envelope's ranges and is 1.7 percent smaller, about 10,750 characters: the 0.9.0 live Haiku month cost 0.46 USD (5,185 input and 245 output tokens per call). Prompt version 8 shows the strategy's whole action and is 12.0 percent smaller again, about 9,460 characters: the 0.10.0 live Haiku months cost 0.41 to 0.43 USD, against a hard worst case of about 0.95 USD (0.98 USD for a 743 hour tuning month). Prompt version 9 adds the shave wording and `shave_now` and is 2.8 percent larger, about 9,720 characters: a Haiku month should cost about 0.42 to 0.44 USD, against a hard worst case of 0.96 to 0.99 USD. Sonnet costs 20 times as much per token: a full Sonnet month should cost roughly 11 to 13 USD, so under a 2.00 USD budget it stops calling the model after about a sixth of the month and the remaining hours use the baseline action. Raise `--budget` (about 14 USD) for a full Sonnet run. Each run logs its exact token cost in `result.json`. ## How to run it @@ -693,10 +740,10 @@ python -m hyperbolic run && python -m hyperbolic compare | Command | What it does | |---|---| | `hyperbolic baseline` | Runs CityLearn's no-control reference. `--month` as for `run`. | -| `hyperbolic run` | Runs the supervisor through the envelope. `--model sonnet`, `--budget 5`, `--hours 24` for a short run, `--month tuning-06`, `tuning-07` or `tuning-08` for a month of the tuning set instead of the phase 2 month (`phase2`, the default), `--scripted` for the 0.4.0 rule-based controller with no API calls (prompt version 3 and the 0.4.0 envelope), `--scripted --prompt-version 4` or `5` for the version 4 or 5 strategy executed literally, `--scripted --prompt-version 6` for the version 5 strategy with the 0.8.0 trim hold, `--scripted --prompt-version 7` or `8` for the version 7 or 8 strategy kept inside the envelope's ranges, `--inject-faults` with `--scripted` to exercise clamps and fallback, `--prompt-version 1` to `7` for the 0.2.0, 0.3.0, 0.4.0, 0.6.0, 0.7.0, 0.8.0 or 0.9.0 prompt. Runs of two days or more need hours + 1 to be a whole number of days (for example 167 or 719), a limit of CityLearn 3.0.2's outage model. | -| `hyperbolic months` | Runs the baseline, the 0.4.0 rule-based controller and the scripted v7 and v8 strategies on every month of the month set (the phase 2 month and the three tuning months), in parallel with no model calls, and prints each KPI per month and the mean. `--controllers`, `--months`, `--run RUN` (repeatable) to show a finished run at the month it was recorded on, `--out` for JSON. | +| `hyperbolic run` | Runs the supervisor through the envelope. `--model sonnet`, `--budget 5`, `--hours 24` for a short run, `--month tuning-06`, `tuning-07` or `tuning-08` for a month of the tuning set instead of the phase 2 month (`phase2`, the default), `--scripted` for the 0.4.0 rule-based controller with no API calls (prompt version 3 and the 0.4.0 envelope), `--scripted --prompt-version 4` or `5` for the version 4 or 5 strategy executed literally, `--scripted --prompt-version 6` for the version 5 strategy with the 0.8.0 trim hold, `--scripted --prompt-version 7`, `8` or `9` for the version 7, 8 or 9 strategy kept inside the envelope's ranges, `--inject-faults` with `--scripted` to exercise clamps and fallback, `--prompt-version 1` to `8` for the 0.2.0, 0.3.0, 0.4.0, 0.6.0, 0.7.0, 0.8.0, 0.9.0 or 0.10.0 prompt. `--month holdout-06`, `holdout-07` or `holdout-08` records a run on the sealed holdout without printing its KPIs (see [HOLDOUT.md](HOLDOUT.md)). Runs of two days or more need hours + 1 to be a whole number of days (for example 167 or 719), a limit of CityLearn 3.0.2's outage model. | +| `hyperbolic months` | Runs the baseline, the 0.4.0 rule-based controller and the scripted v7, v8 and v9 strategies on every month of the month set (the phase 2 month and the three tuning months), in parallel with no model calls, and prints each KPI per month and the mean. `--controllers`, `--months`, `--run RUN` (repeatable) to show a finished run at the month it was recorded on, `--out` for JSON. `--holdout-final --months holdout-06 holdout-07 holdout-08` is the one final scoring of the sealed holdout, on its own; without the flag a holdout month or run is refused. | | `hyperbolic replay RUN_DIR` | Re-runs a saved run from its files with zero model or simulator calls and reports whether every decision and action matches. Exit code 1 if not. | -| `hyperbolic compare` | Pairs the latest baseline (from `runs/` or the committed `results/`) with the latest agent run (a live run wins over a scripted one), prints the KPIs and writes the viewer data. `--pair BASELINE AGENT` to choose, `--out` to write the viewer data elsewhere, `--against RUN` (repeatable) to print another finished run of the same length beside them, for example the rule-based controller; the viewer data is unchanged. | +| `hyperbolic compare` | Pairs the latest baseline (from `runs/` or the committed `results/`) with the latest agent run (a live run wins over a scripted one), prints the KPIs and writes the viewer data. `--pair BASELINE AGENT` to choose, `--out` to write the viewer data elsewhere, `--against RUN` (repeatable) to print another finished run of the same length beside them, for example the rule-based controller; the viewer data is unchanged. A pair on the holdout is refused without `--holdout-final`. | | `hyperbolic serve` | Live mode in the browser on 127.0.0.1 (see [Watch it live](#watch-it-live)). `--port 8765`, `--no-browser`. | `python -m hyperbolic` works the same as `hyperbolic`. A full episode simulates in about 7 seconds; a live run takes as long as 719 model calls. @@ -734,7 +781,7 @@ Dark by default with a light toggle, built for phones first, and it respects red ## Tests -`python -m pytest -q` runs fully offline. Envelope rules and runner logic run against a small fake district for speed; the backend, the CLI and the no-control equivalence run against real CityLearn on the shipped dataset; replay and the viewer builder run against CityLearn runs recorded in `tests/fixtures`. Covered: live mode through the real server with fake models (a full run streamed, recorded and replayed identically, the streamed hours matching the replay viewer's data, pause and resume, stop saving a partial run, the budget cap, the key never appearing in any response or file, the 127.0.0.1 binding and host checks, and the Claude options off without a key), the peak limiter (never above the target in randomized trials, trims recorded as clamps, battery before tank, off for older runs, and a CityLearn run that replays identically with its trims), the combined signal, the peak tracker, the reason length guard, the forecast horizons (6, 12 and 24 hours, the 0.2.0 mislabel), the 24 hour outlook never using unseen data, all eight prompt versions, `EnvelopeConfig` pinned field by field to 0.8.0 and the 0.8.0 and 0.9.0 live months replaying 719 of 719 hours when they are on disk, the deviation classes (every cooling, battery and tank class, the strategy's own reply having none, the tolerance, when each happened), the attribution replays (only the listed points replaced, the control reproducing a recorded CityLearn run exactly, a class put back moving the month toward the strategy, the gap shares), the version 8 observation (the strategy's whole action per building inside the ranges, the dropped fields), the version 8 prompt wording and request size, the warm-room cooling (the ramp, none in an outage, an empty room or with a trim held), the version 8 strategy equal to version 7's without a warm room, the night rule off, the scripted version 8 reply, version 8 runs on the fake district and 24 hours of CityLearn that replay identically, a full version 8 CityLearn month below the baseline's too-warm share, the version 8 search grid and rule, the month set (its months and lengths, a tuning month written as its own dataset, `run --month` recording its month and replaying, the rule-based controller reproducing its tuning month) and the compare report (means only over every month, partial runs marked), the version 7 observation (every trim state with `min_depth` and `end_trim_ok`, both ranges, `last_clamp`, the dropped fields), any value inside the version 7 ranges passing the envelope unclamped in randomized trials, a held trim left to `null` being clamped while the top of its range is not, the version 7 battery plan (version 5's with the shipped numbers, the soft cap winding a discharge down and leaving spare solar alone, valley filling), the version 7 prompt wording and request size, the scripted version 7 reply, version 7 runs on the fake district and 24 hours of CityLearn that replay identically and a full version 7 CityLearn month, the fitting evaluation and the version 7 search grid, the trim hold (on, hold, ease, release, end at the setpoint, in an empty room and with no baseline cooling, early end with a reason, the rounding allowance, logging, fallback, the deviation and comfort rules still first, off for older runs, and runs that replay byte for byte and detect changed hold settings), the version 6 prompt, observation, `end_trim` schema and request size, the scripted version 6 reply following `strategy_battery`, the viewer showing early ends, a 24 hour version 6 CityLearn run that replays identically and a full version 6 CityLearn month, the exact ramping split and the evaluation control reproducing a recorded CityLearn run, the version 4 reference action equal to what the rule-based controller replies, the version 5 smoothing view and strategy (trim hold, slow release, release at the setpoint, gentle charge start, even discharge), a full version 5 CityLearn month with ramping below the baseline, version 5 runs that replay byte for byte and detect a changed observation, the public page's How it works and Run it yourself sections, the spread check (blocked at the top price with its reason, allowed when the coming peak pays, never blocked by a flat weekend, standing losses, spare solar, off for older runs and the rule-based reference), the indoor temperature being the one CityLearn simulated, 24 hour and full-month rule-based dry runs on CityLearn (the full month must still score the 0.4.0 cost, emissions, daily peak, ramping and load factor), a 24 hour version 4 run on CityLearn that replays identically, recorded version 4 runs that replay byte for byte and detect a changed reference policy, a cooling offset moving indoor temperatures, every envelope rule, malformed and schema-breaking replies, fallback on invalid, missing and refused replies, the budget stop, key handling, deterministic replay and tamper detection, replay with CityLearn not even importable, and byte-identical viewer data from the same files in one process or two. +`python -m pytest -q` runs fully offline. Envelope rules and runner logic run against a small fake district for speed; the backend, the CLI and the no-control equivalence run against real CityLearn on the shipped dataset; replay and the viewer builder run against CityLearn runs recorded in `tests/fixtures`. Covered: live mode through the real server with fake models (a full run streamed, recorded and replayed identically, the streamed hours matching the replay viewer's data, pause and resume, stop saving a partial run, the budget cap, the key never appearing in any response or file, the 127.0.0.1 binding and host checks, and the Claude options off without a key), the peak limiter (never above the target in randomized trials, trims recorded as clamps, battery before tank, off for older runs, and a CityLearn run that replays identically with its trims), the combined signal, the peak tracker, the reason length guard, the forecast horizons (6, 12 and 24 hours, the 0.2.0 mislabel), the 24 hour outlook never using unseen data, all eight prompt versions, `EnvelopeConfig` pinned field by field to 0.8.0 and the 0.8.0 and 0.9.0 live months replaying 719 of 719 hours when they are on disk, the deviation classes (every cooling, battery and tank class, the strategy's own reply having none, the tolerance, when each happened), the attribution replays (only the listed points replaced, the control reproducing a recorded CityLearn run exactly, a class put back moving the month toward the strategy, the gap shares), the version 8 observation (the strategy's whole action per building inside the ranges, the dropped fields), the version 8 prompt wording and request size, the warm-room cooling (the ramp, none in an outage, an empty room or with a trim held), the version 8 strategy equal to version 7's without a warm room, the night rule off, the scripted version 8 reply, version 8 runs on the fake district and 24 hours of CityLearn that replay identically, a full version 8 CityLearn month below the baseline's too-warm share, the version 8 search grid and rule, the month set (its months and lengths, a tuning month written as its own dataset, `run --month` recording its month and replaying, the rule-based controller reproducing its tuning month) and the compare report (means only over every month, partial runs marked), the version 7 observation (every trim state with `min_depth` and `end_trim_ok`, both ranges, `last_clamp`, the dropped fields), any value inside the version 7 ranges passing the envelope unclamped in randomized trials, a held trim left to `null` being clamped while the top of its range is not, the version 7 battery plan (version 5's with the shipped numbers, the soft cap winding a discharge down and leaving spare solar alone, valley filling), the version 7 prompt wording and request size, the scripted version 7 reply, version 7 runs on the fake district and 24 hours of CityLearn that replay identically and a full version 7 CityLearn month, the fitting evaluation and the version 7 search grid, the trim hold (on, hold, ease, release, end at the setpoint, in an empty room and with no baseline cooling, early end with a reason, the rounding allowance, logging, fallback, the deviation and comfort rules still first, off for older runs, and runs that replay byte for byte and detect changed hold settings), the version 6 prompt, observation, `end_trim` schema and request size, the scripted version 6 reply following `strategy_battery`, the viewer showing early ends, a 24 hour version 6 CityLearn run that replays identically and a full version 6 CityLearn month, the exact ramping split and the evaluation control reproducing a recorded CityLearn run, the version 4 reference action equal to what the rule-based controller replies, the version 5 smoothing view and strategy (trim hold, slow release, release at the setpoint, gentle charge start, even discharge), a full version 5 CityLearn month with ramping below the baseline, version 5 runs that replay byte for byte and detect a changed observation, the public page's How it works and Run it yourself sections, the spread check (blocked at the top price with its reason, allowed when the coming peak pays, never blocked by a flat weekend, standing losses, spare solar, off for older runs and the rule-based reference), the indoor temperature being the one CityLearn simulated, 24 hour and full-month rule-based dry runs on CityLearn (the full month must still score the 0.4.0 cost, emissions, daily peak, ramping and load factor), a 24 hour version 4 run on CityLearn that replays identically, recorded version 4 runs that replay byte for byte and detect a changed reference policy, a cooling offset moving indoor temperatures, every envelope rule, malformed and schema-breaking replies, fallback on invalid, missing and refused replies, the budget stop, key handling, deterministic replay and tamper detection, replay with CityLearn not even importable, and byte-identical viewer data from the same files in one process or two. From 0.11.0 also: the sealed holdout (its three months kept out of the month set, a schema with only Buildings 4 to 6, the seal matching `HOLDOUT.md` and failing on a changed or extra file, `months`, `compare`, `run_month` and recorded runs refusing it without `--holdout-final`, `run` on it printing no KPIs, and the baseline loading on it), the peak diagnosis (peak hours district and per building, the class ranking, version 8 deviation classes and `warm_skipped`, replays on their own month and never the holdout, a CityLearn run that never shaves), the version 9 acceptance rule case by case with its pick and grid, the version 9 observation, strategy, wording, request size and scripted reply, version 9 runs that replay on the fake district and on 24 hours of CityLearn, and the four 0.10.0 live months replaying hour for hour when they are on disk. CI (GitHub Actions) runs the tests on Python 3.10 and 3.12 with CPU-only torch, a repository rules check, and an author check that fails if any commit in a push or pull request was not authored by AddisonTech. @@ -748,7 +795,10 @@ CI (GitHub Actions) runs the tests on Python 3.10 and 3.12 with CPU-only torch, - **The live agent does not carry a plan from hour to hour.** Each hour is a separate call. In 0.7.0 it was asked to hold cooling trims across hours and broke that in 142 of 237 trim ends (the scripted strategy: 4 of 167). From 0.8.0 the envelope holds them in code, and in the 0.8.0 live month it clamped the agent's cooling 345 times in 292 of 719 hours, 250 of them on `null` replies the prompt said would keep the trim. Version 7 shows the allowed range instead. In the 0.9.0 live month the agent sent `strategy_battery` in 2,150 of 2,157 building-hours but started almost none of the trims the strategy calls for, so version 8 shows the cooling as a number too. Its battery use is still the agent's own: in 0.7.0 that, not the trims, is what raised ramping above 0.6.0. - **The 0.8.0 estimates were not live results.** The 0.7.0 replies sent through the 0.8.0 rules are fixed, so they cannot react to what the hold changed, and they do not include the version 6 prompt. The live month came in lower on ramping than that estimate (1.0076 against 1.0249); this release does not split how much of that is the hold and how much the prompt. - **The 0.10.0 estimates are not live results.** They fill the version 8 strategy into the 0.9.0 live replies, which were written for the version 7 prompt and cannot react to what changed. The month set's scripted columns are not model results either. The live months that followed came in close to the scripted v8 strategy (mean cost 0.9122 against 0.9087). -- **The phase 2 month is no longer a clean holdout.** The warm-room cooling's outage exception and the selection rule were set after looking at it, so from 0.10.0 the three tuning months are the cleaner evidence. +- **The phase 2 month is no longer a clean holdout.** The warm-room cooling's outage exception and the selection rule were set after looking at it, so from 0.10.0 the three tuning months are the cleaner evidence. In 0.11.0 the version 9 search ran on all four months, so none of them is held out any more. +- **The holdout is the closest available, not a clean one.** Buildings 4 to 6 of phase 3 set 1 are new buildings (loads, occupancy, LSTM models, batteries, heat pumps) on the tuning months' weather, tariff and carbon intensity, so a good result there says the agent and strategy carry to other buildings, not to another climate or price structure. The seal keeps the project's tools from scoring it by accident; a run's `result.json` still holds its KPIs. +- **The version 9 strategy's gain is small and came from a second pass.** It lowers the four-month all-time peak by 0.003 and ramping by 0.0016 for 0.0006 of cost, and the passing setting was found in a finer grid added after the first found none. The holdout is where that should be checked. +- **The 0.11.0 estimates are not live results.** v9 scripted is the strategy run literally, and the replays send the 0.10.0 live replies again with classes put back; whether Haiku shaves after the top price and starts trims under the version 9 wording can only be measured live. - **The warm-room cooling rests on CityLearn's LSTM.** It answers a mechanism seen in the simulator: a trimmed room loses the baseline's cushion and a hot morning or an outage carries it over the band. On the tuning months it removed 80 percent of the strategy's too-warm hours for about 0.002 of cost. - **The 0.9.0 estimates are not live results.** They fit the 0.8.0 live replies into the version 7 ranges and, in one column, swap in `strategy_battery`. They assume a model that obeys the ranges and the battery plan, and fixed replies cannot react to what changed. Whether Haiku does that under the version 7 wording can only be measured live. - **Tuned on one other dataset.** From 0.6.0 the strategy's numbers come from three summer months of the CityLearn 2023 online evaluation set: other weather, loads, carbon and tariff, but the same three buildings and LSTM models, so it says nothing about other building types. The 0.4.0 rule-based controller still carries settings chosen on the first week of the reported month. @@ -762,4 +812,4 @@ CI (GitHub Actions) runs the tests on Python 3.10 and 3.12 with CPU-only torch, MIT, see `LICENSE`. -CityLearn is developed by the Intelligent Environments Laboratory and contributors and is distributed under the MIT license. The dataset in `hyperbolic/data/citylearn_challenge_2023_phase_2_local_evaluation` is copied unchanged from the CityLearn repository (tag v3.0.2); its license is in `hyperbolic/data/CITYLEARN_LICENSE.txt`. The tuning dataset in `scripts/tuning_data/citylearn_challenge_2023_phase_2_online_evaluation_1` is copied unchanged from the same repository and tag, under the same license. Hyperbolic is not affiliated with the CityLearn project. +CityLearn is developed by the Intelligent Environments Laboratory and contributors and is distributed under the MIT license. The dataset in `hyperbolic/data/citylearn_challenge_2023_phase_2_local_evaluation` is copied unchanged from the CityLearn repository (tag v3.0.2); its license is in `hyperbolic/data/CITYLEARN_LICENSE.txt`. The tuning dataset in `scripts/tuning_data/citylearn_challenge_2023_phase_2_online_evaluation_1` and the holdout dataset in `holdout/citylearn_challenge_2023_phase_3_1` are copied unchanged from the same repository and tag, under the same license. Hyperbolic is not affiliated with the CityLearn project. diff --git a/docs/assets/version.js b/docs/assets/version.js index 465f1c6..8da2229 100644 --- a/docs/assets/version.js +++ b/docs/assets/version.js @@ -1,3 +1,3 @@ // The package version shown in the viewer footer. Kept equal to hyperbolic.__version__ // by tests/test_viewer.py; refresh it with: python -m hyperbolic.viewer --write-version -window.HYPERBOLIC_VERSION = "0.10.0"; +window.HYPERBOLIC_VERSION = "0.11.0"; diff --git a/holdout/citylearn_challenge_2023_phase_3_1/Building_1.csv b/holdout/citylearn_challenge_2023_phase_3_1/Building_1.csv new file mode 100644 index 0000000..099f16f --- /dev/null +++ b/holdout/citylearn_challenge_2023_phase_3_1/Building_1.csv @@ -0,0 +1,2209 @@ +month,hour,day_type,daylight_savings_status,indoor_dry_bulb_temperature,average_unmet_cooling_setpoint_difference,indoor_relative_humidity,non_shiftable_load,dhw_demand,cooling_demand,heating_demand,solar_generation,occupant_count,indoor_dry_bulb_temperature_cooling_set_point,indoor_dry_bulb_temperature_heating_set_point,hvac_mode +6,1,2,0,22.428127,-0.79409474,78.008,0.3562692,0.055730477,0.0,0.0,0.0,3.0,23.222221,23.222221,1 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a/holdout/citylearn_challenge_2023_phase_3_1/Building_1.pth b/holdout/citylearn_challenge_2023_phase_3_1/Building_1.pth new file mode 100644 index 0000000..21b085e Binary files /dev/null and b/holdout/citylearn_challenge_2023_phase_3_1/Building_1.pth differ diff --git a/holdout/citylearn_challenge_2023_phase_3_1/Building_2.csv b/holdout/citylearn_challenge_2023_phase_3_1/Building_2.csv new file mode 100644 index 0000000..d79f5c9 --- /dev/null +++ b/holdout/citylearn_challenge_2023_phase_3_1/Building_2.csv @@ -0,0 +1,2209 @@ +month,hour,day_type,daylight_savings_status,indoor_dry_bulb_temperature,average_unmet_cooling_setpoint_difference,indoor_relative_humidity,non_shiftable_load,dhw_demand,cooling_demand,heating_demand,solar_generation,occupant_count,indoor_dry_bulb_temperature_cooling_set_point,indoor_dry_bulb_temperature_heating_set_point,hvac_mode +6,1,2,0,22.992903,-1.451542,84.7176,0.1884836,0.0,0.0,0.0,0.0,1.0,24.444445,24.444445,1 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b/holdout/citylearn_challenge_2023_phase_3_1/Building_4.csv new file mode 100644 index 0000000..10848b9 --- /dev/null +++ b/holdout/citylearn_challenge_2023_phase_3_1/Building_4.csv @@ -0,0 +1,2209 @@ +month,hour,day_type,daylight_savings_status,indoor_dry_bulb_temperature,average_unmet_cooling_setpoint_difference,indoor_relative_humidity,non_shiftable_load,dhw_demand,cooling_demand,heating_demand,solar_generation,occupant_count,indoor_dry_bulb_temperature_cooling_set_point,indoor_dry_bulb_temperature_heating_set_point,hvac_mode +6,1,2,0,25.957903,-1.2643192,70.15945,0.5966637,0.0,0.0,0.0,0.0,2.0,27.222221,27.222221,1 +6,2,2,0,25.708092,-1.5141302,71.391266,0.59429103,0.0,0.0,0.0,0.0,2.0,27.222221,27.222221,1 +6,3,2,0,25.436514,-1.7857076,72.76189,0.5919184,0.0,0.0,0.0,0.0,2.0,27.222221,27.222221,1 +6,4,2,0,25.189693,-2.0325286,74.03752,0.5895458,0.0,0.0,0.0,0.0,2.0,27.222221,27.222221,1 +6,5,2,0,24.739435,-1.9272323,76.11142,0.58717316,0.0,0.0,0.0,0.0,2.0,26.666666,26.666666,1 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default=Path("runs"), help="where run folders go (default: runs)") - p.add_argument("--month", default=mo.PHASE2, choices=[m.key for m in mo.months()], - help=f"which month of the month set to run (default: {mo.PHASE2}, the reported month)") + p.add_argument("--month", default=mo.PHASE2, choices=[m.key for m in mo.all_months()], + help=f"which month of the month set to run (default: {mo.PHASE2}, the reported month), or a " + "month of the sealed holdout, whose agent KPIs are not printed (see HOLDOUT.md)") def _sim(control: bool, schema: Path | None = None): @@ -107,6 +108,12 @@ def cmd_run(args) -> int: f"budget ${args.budget:.2f} -> {out}") res = run_session(sim, ctl, RunWriter(out), args.hours, {"kind": "citylearn", "control": True, "month": month.key}, dataset=month.dataset) + if month.holdout: + print(f" done: {res['steps']} hours on the sealed holdout; KPIs are not shown. Score it once with " + f"hyperbolic months {mo.HOLDOUT_FLAG} (see HOLDOUT.md).") + if res.get("calls"): + print(f" model calls {res['calls']}, fallback {res['fallback_steps']}, cost ${res['cost_usd']:.4f}") + return 0 _print_result(res) return 0 @@ -132,7 +139,11 @@ def cmd_compare(args) -> int: print("no matching baseline and agent runs found; run `hyperbolic baseline` and `hyperbolic run` first", file=sys.stderr) return 1 - data = write_viewer_data(pair, args.out) + try: + data = write_viewer_data(pair, args.out, holdout_final=args.holdout_final) + except mo.HoldoutSealed as exc: + print(f"error: {exc}", file=sys.stderr) + return 2 s = data["scenarios"][0] others = [] for d in args.against or []: @@ -164,15 +175,26 @@ def cmd_months(args) -> int: import multiprocessing as mp keys = args.months or [m.key for m in mo.months()] + try: + mo.guard(keys, args.holdout_final) + if args.holdout_final and not all(mo.is_holdout(k) for k in keys): + raise mo.HoldoutSealed(f"{mo.HOLDOUT_FLAG} scores the holdout months on their own; " + f"pass only {', '.join(m.key for m in mo.holdout_months())} with --months") + except mo.HoldoutSealed as exc: + print(f"error: {exc}", file=sys.stderr) + return 2 rows = [] for d in args.run or []: try: - rows.append(mo.recorded_run(Path(d))) + rows.append(mo.recorded_run(Path(d), args.holdout_final)) + except mo.HoldoutSealed as exc: + print(f"error: --run {d}: {exc}", file=sys.stderr) + return 2 except (OSError, ValueError, KeyError) as exc: print(f"error: --run {d}: {exc}", file=sys.stderr) return 1 with tempfile.TemporaryDirectory() as tmp: - jobs = [(c, k, tmp) for c in args.controllers for k in keys] + jobs = [(c, k, tmp, args.holdout_final) for c in args.controllers for k in keys] print(f"{len(jobs)} CityLearn episodes ({', '.join(args.controllers)} on {', '.join(keys)}), no model calls") # spawn, not fork: forking after torch has started its threads can deadlock. with cf.ProcessPoolExecutor(max_workers=args.jobs, mp_context=mp.get_context("spawn")) as pool: @@ -255,8 +277,8 @@ def build_parser() -> argparse.ArgumentParser: help=f"observation and prompt version (default: {PROMPT_VERSION}; with --scripted, " f"{RULE_PROMPT_VERSION}, the 0.4.0 rule-based controller, while versions 4 and 5 " "run their strategies literally, version 6 runs the version 5 strategy with the " - "trim hold, version 7 keeps it inside the ranges its observation shows and version 8 " - "adds extra cooling in a warm room)") + "trim hold, version 7 keeps it inside the ranges its observation shows, version 8 " + "adds extra cooling in a warm room and version 9 keeps charge for the evening shave)") r.set_defaults(func=cmd_run) rp = sub.add_parser("replay", help="re-run a saved run from its files with zero model or simulator calls") @@ -271,13 +293,18 @@ def build_parser() -> argparse.ArgumentParser: c.add_argument("--against", action="append", metavar="AGENT_DIR", help="also print this finished agent run's KPIs, for example the rule-based controller " "(repeatable; the viewer data is unchanged)") + c.add_argument(mo.HOLDOUT_FLAG, dest="holdout_final", action="store_true", + help="allow a pair on the sealed holdout: the one final scoring (see HOLDOUT.md)") c.set_defaults(func=cmd_compare) mm = sub.add_parser("months", help="run offline controllers on every month of the month set and compare them") mm.add_argument("--controllers", nargs="+", default=list(mo.CONTROLLERS), choices=list(mo.CONTROLLERS), help="offline controllers to run (default: all of them)") - mm.add_argument("--months", nargs="+", default=None, choices=[m.key for m in mo.months()], - help="months to run (default: every month available locally)") + mm.add_argument("--months", nargs="+", default=None, choices=[m.key for m in mo.all_months()], + help="months to run (default: every month of the month set available locally; the " + "holdout months only with --holdout-final)") + mm.add_argument(mo.HOLDOUT_FLAG, dest="holdout_final", action="store_true", + help="score the sealed holdout months, on their own: the one final scoring (see HOLDOUT.md)") mm.add_argument("--run", action="append", metavar="RUN_DIR", help="also show this finished run at the month it was recorded on (repeatable)") mm.add_argument("--jobs", type=int, default=None, help="parallel simulations (default: one per CPU)") diff --git a/hyperbolic/live.py b/hyperbolic/live.py index 723d0e3..71513c6 100644 --- a/hyperbolic/live.py +++ b/hyperbolic/live.py @@ -75,10 +75,10 @@ {"id": "week", "label": "1 week (167 hours)", "hours": 167}, {"id": "month", "label": "Full month (719 hours)", "hours": dc.EPISODE_HOURS}, ] -# Typical prompt version 8 request and reply, for a cost estimate before any run is recorded: the -# 0.9.0 live month averaged 5,185 input and 245 output tokens on version 7, version 8 requests are -# 12 percent smaller, and replies that send the strategy's cooling as a number may run a little longer. -TYPICAL_TOKENS = (4560, 260) +# Typical prompt version 9 request and reply, for a cost estimate before any run is recorded: the +# 0.10.0 live months averaged 4,676 input and 215 output tokens on version 8, and version 9 requests +# are 2.8 percent larger. +TYPICAL_TOKENS = (4810, 220) def key_status(env: dict[str, str] | None = None) -> dict: diff --git a/hyperbolic/model.py b/hyperbolic/model.py index e331206..4f0f8d3 100644 --- a/hyperbolic/model.py +++ b/hyperbolic/model.py @@ -314,6 +314,20 @@ class StrategyParamsV8(StrategyParamsV7): # shipped: it raised cost and cleared no warm hour +@dataclass(frozen=True) +class StrategyParamsV9(StrategyParamsV8): + """Numbers in the prompt version 9 strategy: version 8's, with a little charge kept back through the + top-price hours for the evening shave. The 0.10.0 all-time peaks fell in an evening hour just after + the top-price discharge, with every battery at its floor. strategy_plan_v9 executes it. Chosen on the + four months of the month set (scripts/tune_strategy.py --version 9) under the 0.11.0 acceptance + rule: the only one of 44 settings (found in a finer second pass, after the first 36 passed none) + with a lower all-time peak and ramping than version 8 at no more than 0.001 of cost, emissions, + discomfort or too warm; larger reserves cut the peak further but cost about 0.003 per 0.05.""" + + evening_reserve: float = 0.01 # state of charge kept above the floor in the top-price hours, so the + # evening shave has something to discharge + + @dataclass(frozen=True) class RuleParamsV2: """Knobs of the 0.3.0 rule-based policy for prompt version 2 observations, kept as tuned @@ -351,7 +365,8 @@ class ScriptedSupervisor: (strategy_plan_v5). Version 6 runs the same version 5 strategy, never ends a trim early, and leaves holding trims to the envelope. Version 7 (strategy_plan_v7) keeps every action inside the ranges the observation shows and uses the version 7 battery plan; version 8 - (strategy_plan_v8) adds extra cooling in a warm room. Version 2 + (strategy_plan_v8) adds extra cooling in a warm room, and version 9 (strategy_plan_v9) keeps a + little charge for the evening shave. Version 2 observations get the 0.3.0 rule and version 1 the simpler 0.2.0 rule. Faults are opt-in: on listed steps it injects a bad reply so the envelope and fallback @@ -364,7 +379,7 @@ def __init__(self, model_key: str = DEFAULT_MODEL, faults: dict[int, str] | None tokens: tuple[int, int] = (0, 0), params: RuleParams | None = None, params_v2: RuleParamsV2 | None = None, strategy: StrategyParams | None = None, strategy_v5: StrategyParamsV5 | None = None, strategy_v7: StrategyParamsV7 | None = None, - strategy_v8: StrategyParamsV8 | None = None) -> None: + strategy_v8: StrategyParamsV8 | None = None, strategy_v9: StrategyParamsV9 | None = None) -> None: self.model_key = model_key self.faults = dict(faults or {}) self.tokens = tokens @@ -374,6 +389,7 @@ def __init__(self, model_key: str = DEFAULT_MODEL, faults: dict[int, str] | None self.strategy_v5 = strategy_v5 or StrategyParamsV5() self.strategy_v7 = strategy_v7 or StrategyParamsV7() self.strategy_v8 = strategy_v8 or StrategyParamsV8() + self.strategy_v9 = strategy_v9 or StrategyParamsV9() @staticmethod def default_faults(hours: int) -> dict[int, str]: @@ -395,7 +411,9 @@ def call(self, step: int, request: dict) -> CallResult: return CallResult('{"reason": "charge batteries", "Building_1": {"battery": 0.4', *self.tokens, stop_reason="end_turn", model="scripted") first = next(iter(obs["buildings"].values()), {}) - if "strategy" in first: # version 8 + if "strategy" in first and "shave_now" in obs["peak"]: # version 9 + reply = strategy_plan_v9(obs, self.strategy_v9) + elif "strategy" in first: # version 8 reply = strategy_plan_v8(obs, self.strategy_v8) elif "cooling_range" in first: # version 7 reply = strategy_plan_v7(obs, self.strategy_v7) @@ -909,6 +927,13 @@ def strategy_plan_v8(obs: dict, p: StrategyParamsV8 | None = None) -> dict: return reply +def strategy_plan_v9(obs: dict, p: StrategyParamsV9 | None = None) -> dict: + """The prompt version 9 strategy: version 8's, keeping evening_reserve of charge above the floor + through the top-price hours, so the shave after them still has charge. Prompt version 9 shows + each building's result as strategy.""" + return strategy_plan_v8(obs, p or StrategyParamsV9()) + + class HoldSupervisor: """Answers every hour with null actions, so the envelope applies the baseline action everywhere: the no-control reference run through the agent path. Used by the live diff --git a/hyperbolic/months.py b/hyperbolic/months.py index da365b9..08a71b8 100644 --- a/hyperbolic/months.py +++ b/hyperbolic/months.py @@ -9,11 +9,18 @@ A tuning month is written out as its own dataset folder (its rows of every series, the LSTM models, and a schema that ends at its last hour), so CityLearn runs it exactly as scripts/tune_strategy.py always has. Every KPI is CityLearn's own, normalized against the no-control run of the same month. + +The holdout (0.11.0, see HOLDOUT.md) is kept apart from the month set: Buildings 4, 5 and 6 of +citylearn_challenge_2023_phase_3_1, June, July and August, each its own episode (holdout-06, +holdout-07, holdout-08). months() never lists it, so nothing that searches or reports the month set +sees it, and scoring it needs final=True (the --holdout-final flag) on files whose hashes still match +the seal. """ from __future__ import annotations import csv +import hashlib import json import shutil from dataclasses import dataclass @@ -21,11 +28,40 @@ from . import district as dc -TUNING_DATASET = (Path(__file__).resolve().parents[1] / "scripts" / "tuning_data" - / "citylearn_challenge_2023_phase_2_online_evaluation_1") +ROOT = Path(__file__).resolve().parents[1] +TUNING_DATASET = ROOT / "scripts" / "tuning_data" / "citylearn_challenge_2023_phase_2_online_evaluation_1" SERIES = ("Building_1.csv", "Building_2.csv", "Building_3.csv", "carbon_intensity.csv", "pricing.csv", "weather.csv") +SHARED_SERIES = ("carbon_intensity.csv", "pricing.csv", "weather.csv") PHASE2 = "phase2" +HOLDOUT_DATASET = ROOT / "holdout" / "citylearn_challenge_2023_phase_3_1" +HOLDOUT_BUILDINGS = ("Building_4", "Building_5", "Building_6") +HOLDOUT_FLAG = "--holdout-final" +# The seal: sha256 of every holdout file as copied from CityLearn v3.0.2, text files with LF line +# endings so a Windows checkout hashes the same as Linux. +HOLDOUT_SHA256 = { + "Building_1.csv": "a7b27f4a3622305e60da0a32a92d468563705f69ebdec2c13d0dc6b2fd705ac1", + "Building_1.pth": "15e137b9e1930370708346d1298ca1f8b937cdb7c8e47e43f2555e543528d394", + "Building_2.csv": "2993728aaf8e270abaa90f08a7aec4e66f584f58927ce6d6dfe5d9582929e109", + "Building_2.pth": "36515f53e995061148697327ee416ed305c756418584f430597616399342a0a3", + "Building_3.csv": "aca204f9943850e794021450a23ed97407a9fed07fc43c6b14123e6c7667ab0a", + "Building_3.pth": "42d10bf54ea210d0626d1d55568f0ad7a0b9fb600f5e2802420a3a4d77168794", + "Building_4.csv": "89d94801d834ce1409ca360b661357176a73834a9f5a90b2345528ba320bd956", + "Building_4.pth": "4eabbb6c24e2c0482c55768541dce6c1f75e5aedcbaf01ae5c8786177a96d974", + "Building_5.csv": "9c5a9a91dd4cb22ffa96c0dad75a995f33a6cda95cab1fd3dfafbf5e2e1da672", + "Building_5.pth": "f0e39abe58629e28d9cbaf8ffdadf41163aa97a0ec5763608d3028c1e47b22ae", + "Building_6.csv": "ecc75e8eb17a4b9e6d28e90cfc8892a596b18197e9d84bbb492b1c050b93bf71", + "Building_6.pth": "d349f9cf27c6ebad5a226c81e7729c0050bdb974793acfbd1cfbf1ff600ee2b6", + "carbon_intensity.csv": "f106216393efe4eee30a5b85823f1fddff2676a09a58a3f63cd28482220516ad", + "pricing.csv": "f06036bdb7b72d4bf13547da6f8453ab9f7095c5fabcf2b6929f93a79e1d0126", + "schema.json": "500bdd747f45d8b63295a5ce7ee557ffda930d25210c32c591ef15d23e3306af", + "weather.csv": "aeb1420f6102c5df2bddc55f211d41cf16e5f572a6fcce3cce8ad37b007875ce", +} + + +class HoldoutSealed(ValueError): + """The holdout was asked for without --holdout-final, or its files no longer match the seal.""" + # The KPIs the report shows, in order. REPORT_KPIS = ("cost_total", "carbon_emissions_total", "electricity_consumption_total", "discomfort_proportion", "discomfort_cold_proportion", "discomfort_hot_proportion", "ramping_average", "daily_peak_average", @@ -38,13 +74,16 @@ class Month: key: str # phase2, tuning-06, ... dataset: str # recorded in run manifests and folder names label: str - rows: tuple[int, int] | None = None # the tuning dataset's rows [start, end) for a tuning month + rows: tuple[int, int] | None = None # the source dataset's rows [start, end) for a tuning or holdout month + source: Path | None = None # that dataset's folder (the tuning dataset when None) + buildings: tuple[str, ...] | None = None # the buildings kept from it (all of them when None) + holdout: bool = False -def _tuning_spans() -> list[tuple[str, int, int]]: - if not (TUNING_DATASET / "Building_1.csv").exists(): +def _spans(dataset: Path, building: str = "Building_1") -> list[tuple[str, int, int]]: + if not (dataset / f"{building}.csv").exists(): return [] - with (TUNING_DATASET / "Building_1.csv").open(encoding="utf-8") as f: + with (dataset / f"{building}.csv").open(encoding="utf-8") as f: months = [row["month"] for row in csv.DictReader(f)] spans, start = [], 0 for i in range(1, len(months) + 1): @@ -54,8 +93,12 @@ def _tuning_spans() -> list[tuple[str, int, int]]: return spans +def _tuning_spans() -> list[tuple[str, int, int]]: + return _spans(TUNING_DATASET) + + def months() -> list[Month]: - """The phase 2 month, then every tuning month available locally.""" + """The phase 2 month, then every tuning month available locally. Never the holdout.""" out = [Month(PHASE2, dc.DATASET, "phase 2")] for month, a, b in _tuning_spans(): key = f"tuning-{int(month):02d}" @@ -63,26 +106,78 @@ def months() -> list[Month]: return out +def holdout_months() -> list[Month]: + """The sealed holdout months, kept out of months().""" + out = [] + for month, a, b in _spans(HOLDOUT_DATASET, HOLDOUT_BUILDINGS[0]): + key = f"holdout-{int(month):02d}" + out.append(Month(key, f"{HOLDOUT_DATASET.name}_buildings_4_5_6_month_{int(month):02d}", key, (a, b), + HOLDOUT_DATASET, HOLDOUT_BUILDINGS, True)) + return out + + +def all_months() -> list[Month]: + return months() + holdout_months() + + def month(key: str) -> Month: - for m in months(): + for m in all_months(): if m.key == key: return m - raise ValueError(f"unknown month {key!r}; available: {', '.join(m.key for m in months())}") + raise ValueError(f"unknown month {key!r}; available: {', '.join(m.key for m in all_months())}") + + +def is_holdout(name: str | None) -> bool: + """Whether a month key or a recorded dataset name is a holdout month.""" + return any(name in (m.key, m.dataset) for m in holdout_months()) + + +def _file_sha256(path: Path) -> str: + data = path.read_bytes() + if path.suffix in (".csv", ".json"): + data = data.replace(b"\r\n", b"\n") + return hashlib.sha256(data).hexdigest() + + +def check_sealed(folder: Path = HOLDOUT_DATASET) -> None: + """Raise HoldoutSealed unless every holdout file is present and matches the seal.""" + bad = [name for name, sha in sorted(HOLDOUT_SHA256.items()) + if not (folder / name).exists() or _file_sha256(folder / name) != sha] + extra = sorted(p.name for p in folder.iterdir() if p.name not in HOLDOUT_SHA256) if folder.exists() else [] + if bad or extra: + raise HoldoutSealed(f"holdout files do not match the seal in HOLDOUT.md: {', '.join(bad + extra)}") + + +def guard(names=(), final: bool = False) -> None: + """Refuse to score a holdout month (key or dataset name) unless final, and only on sealed files.""" + held = sorted({n for n in names if is_holdout(n)}) + if not held: + return + if not final: + raise HoldoutSealed(f"{', '.join(held)} is the sealed holdout; it is scored once, for the final " + f"result, with {HOLDOUT_FLAG} (see HOLDOUT.md)") + check_sealed() def month_schema(m: Month, out: Path) -> Path: - """The schema CityLearn runs for month m. A tuning month is written into out/.""" + """The schema CityLearn runs for month m. A tuning or holdout month is written into out/, + with only its own buildings.""" if m.rows is None: return dc.schema_path() + src = m.source or TUNING_DATASET a, b = m.rows d = Path(out) / m.key d.mkdir(parents=True, exist_ok=True) - for name in SERIES: - lines = (TUNING_DATASET / name).read_text(encoding="utf-8").splitlines(keepends=True) + series = SERIES if m.buildings is None else (*(f"{x}.csv" for x in m.buildings), *SHARED_SERIES) + for name in series: + lines = (src / name).read_text(encoding="utf-8").splitlines(keepends=True) (d / name).write_text("".join([lines[0], *lines[1 + a:1 + b]]), encoding="utf-8") - for pth in TUNING_DATASET.glob("*.pth"): - shutil.copy(pth, d / pth.name) - schema = json.loads((TUNING_DATASET / "schema.json").read_text(encoding="utf-8")) + for pth in src.glob("*.pth"): + if m.buildings is None or pth.stem in m.buildings: + shutil.copy(pth, d / pth.name) + schema = json.loads((src / "schema.json").read_text(encoding="utf-8")) + if m.buildings is not None: + schema["buildings"] = {k: v for k, v in schema["buildings"].items() if k in m.buildings} (d / "schema.json").write_text(json.dumps({**schema, "simulation_start_time_step": 0, "simulation_end_time_step": b - a - 1}), encoding="utf-8") return d / "schema.json" @@ -101,11 +196,13 @@ def hours(m: Month) -> int: "rule": ("0.4.0 rule-based", 3, True), "v7": ("v7 scripted", 7, True), "v8": ("v8 scripted", 8, True), + "v9": ("v9 scripted", 9, True), } -def run_month(job: tuple[str, str, str]) -> dict: - """job: (controller key, month key, scratch folder). One CityLearn episode, no model calls.""" +def run_month(job: tuple) -> dict: + """job: (controller key, month key, scratch folder[, final]). One CityLearn episode, no model calls. + A holdout month needs final True.""" import torch torch.set_num_threads(1) # one simulation per process @@ -114,7 +211,8 @@ def run_month(job: tuple[str, str, str]) -> dict: from .runner import AgentController, BaselineController, NullWriter, controller_settings, run_session from .sim import CityLearnSimulator - kind, key, scratch = job + kind, key, scratch = job[:3] + guard([key], final=bool(job[3]) if len(job) > 3 else False) m = month(key) schema = month_schema(m, Path(scratch)) if kind == "baseline": @@ -130,13 +228,15 @@ def run_month(job: tuple[str, str, str]) -> dict: "clamped": res["clamped_steps"], "fallback": res["fallback_steps"]} -def recorded_run(run_dir: Path) -> dict: - """A finished run's KPIs for the report, placed at the month its manifest records.""" +def recorded_run(run_dir: Path, final: bool = False) -> dict: + """A finished run's KPIs for the report, placed at the month its manifest records. A run on a + holdout month needs final True.""" m = json.loads((run_dir / "manifest.json").read_text(encoding="utf-8")) - r = json.loads((run_dir / "result.json").read_text(encoding="utf-8")) - by_dataset = {x.dataset: x.key for x in months()} + by_dataset = {x.dataset: x.key for x in all_months()} if m.get("dataset") not in by_dataset: raise ValueError(f"{run_dir.name}: dataset {m.get('dataset')!r} is not in the month set") + guard([m["dataset"]], final) + r = json.loads((run_dir / "result.json").read_text(encoding="utf-8")) sup = m.get("supervisor") or {} if sup.get("kind") == "live": label = f"{sup.get('model_key')} v{sup.get('prompt_version')}" @@ -152,7 +252,7 @@ def recorded_run(run_dir: Path) -> dict: def report(rows: list[dict]) -> dict: """Per controller: each month's KPIs and, when it ran every month in the report, their mean.""" - keys = [m.key for m in months()] + keys = [m.key for m in all_months()] out: dict = {} for r in rows: out.setdefault(r["controller"], {})[r["month"]] = r @@ -171,7 +271,7 @@ def report(rows: list[dict]) -> dict: def table(rep: dict) -> str: """One block per KPI: a row per controller, a column per month and the mean.""" - cols = [m.key for m in months() if any(m.key in by_month for by_month in rep.values())] + ["mean"] + cols = [m.key for m in all_months() if any(m.key in by_month for by_month in rep.values())] + ["mean"] width = max([len(c) for c in rep] + [10]) + 2 lines = [f"{'CityLearn KPI (district)':<{width}}" + "".join(f"{c:>11}" for c in cols)] for x in (*REPORT_KPIS, "clamped"): diff --git a/hyperbolic/prompt.py b/hyperbolic/prompt.py index fbc8c3e..06aa0f4 100644 --- a/hyperbolic/prompt.py +++ b/hyperbolic/prompt.py @@ -39,6 +39,13 @@ 0.9.0 live month followed strategy_battery but rarely started a cooling trim itself. The strategy adds extra cooling in an occupied room that runs warm (the hot-side fix), and signal_outlook is dropped. The envelope's rules are the same as for version 6. + 9 v0.11.0. The 0.10.0 live months sent strategy almost everywhere, but sent null where it + started a trim (state off) and left the batteries idle where it shaved after the top-price + hours or on a flat-price day, as the version 8 wording allowed ("Before them discharge only to + shave", "Idle on flat_24h days"); the July and August all-time peaks fell in such hours. + Version 9 says to send every number, a trim start and a small shave too, and to shave in any + hour peak.shave_now (new) is true, and its strategy keeps a little charge for that shave. The + envelope's rules are the same as for version 6. """ from __future__ import annotations @@ -56,16 +63,18 @@ StrategyParamsV5, StrategyParamsV7, StrategyParamsV8, + StrategyParamsV9, battery_plan_v5, battery_plan_v7, rule_plan_v3, strategy_plan_v8, + strategy_plan_v9, ) from .signals import charge_spread, combined_signal -PROMPT_VERSION = 8 -PROMPT_VERSIONS = (1, 2, 3, 4, 5, 6, 7, 8) +PROMPT_VERSION = 9 +PROMPT_VERSIONS = (1, 2, 3, 4, 5, 6, 7, 8, 9) # From this version the reply may end a held cooling trim early (envelope trim hold, 0.8.0). END_TRIM_FROM = 6 @@ -923,13 +932,13 @@ def system_prompt_v7(cfg: EnvelopeConfig, buildings: tuple[str, ...]) -> str: def build_observation_v8(state: dict, ctx: StepContext, last: Decision | None, look: Lookahead, - envelope: Envelope | None) -> dict: + envelope: Envelope | None, strategy=strategy_plan_v8) -> dict: """Version 8: version 7 with the version 8 strategy's whole action per building (strategy, with strategy_reason) in place of strategy_battery, and without signal_outlook, which no version 7 or 8 strategy reads.""" obs = build_observation_v7(state, ctx, last, look, envelope) del obs["strategy_battery_reason"], obs["signal_outlook"] - plan = strategy_plan_v8(obs) + plan = strategy(obs) obs["strategy_reason"] = plan["reason"] for b, ob in obs["buildings"].items(): del ob["strategy_battery"] @@ -995,6 +1004,77 @@ def system_prompt_v8(cfg: EnvelopeConfig, buildings: tuple[str, ...]) -> str: reply invalid.""" +def build_observation_v9(state: dict, ctx: StepContext, last: Decision | None, look: Lookahead, + envelope: Envelope | None) -> dict: + """Version 9: version 8 with the version 9 strategy (charge kept for the evening shave) and + peak.shave_now, the strategy's own test for a peak-shaving hour, at any time of day.""" + obs = build_observation_v8(state, ctx, last, look, envelope, strategy=strategy_plan_v9) + pk, sp = obs["peak"], StrategyParamsV9() + now, forecast = pk["district_load_now_kwh"], pk["forecast_today_kwh"] + pk["shave_now"] = bool(sp.shave > 0 and now is not None and forecast and now >= sp.shave * forecast) + return obs + + +def system_prompt_v9(cfg: EnvelopeConfig, buildings: tuple[str, ...]) -> str: + peak = ", battery then tank charging above peak.target_kwh" if cfg.peak_limit else "" + spread = ", grid charging when grid_charging_ok is false (spare solar may still be stored)" if cfg.spread_check else "" + sp = StrategyParamsV9() # the strategy's numbers, chosen on the month set + full = sp.cold_margin_c + sp.cold_ramp_c + return f"""You are the hourly supervisory controller for a simulated district of {len(buildings)} \ +buildings (CityLearn 2023 challenge data). Each building has a heat pump for space cooling, a hot water \ +tank, a battery and rooftop solar. Every hour you choose one action per device for every building. + +Goals, in this order, graded by CityLearn's own KPIs over the month against the no-control reference \ +(storage idle, heat pump meeting the ideal load): +1. Comfort: no more discomfort, too cool or too warm, than the reference. Discomfort is any occupied \ +hour with indoor temperature outside setpoint +/- {cfg.comfort_band_k:g} C. +2. Peaks and smoothness: the district's daily and monthly peak, daily load factor and ramping at or \ +better than the reference. Ramping counts every hour the district draws more than the hour before. +3. Cost first, then carbon. Each building's cost counts equally, whatever its size. + +What you see: +- strategy in each building: the action the strategy below gives this hour for cooling, dhw and \ +battery, worked out in code inside the envelope's ranges, with strategy_reason. Send it as given, every \ +number exactly: battery -0.033 is not 0, and cooling under baseline_action is not null, trim state off \ +too. Where you differ, say why. +- cooling_range and battery_range: the values the safety envelope lets through this hour without a \ +clamp. cooling_range null: only the baseline passes. +- trim: the cooling trim the envelope carries: depth below baseline_action, state (off, hold, ease, \ +end), min_depth, end_trim_ok. last_clamp: what the envelope changed there last hour, and why. +- price_plan, battery, indoor_vs_setpoint_c, smoothing, peak and next_24h: what the strategy reads. + +Strategy (strategy already applies it): +1. Cooling. An occupied room more than {sp.cold_margin_c:g} C below its setpoint is overcooled: cooling \ +goes below baseline_action, from 0 at {sp.cold_margin_c:g} C below to {sp.cold_trim:g} at {full:g} C or more \ +below. That starts a trim when state is off; while it is held, cooling is a number inside cooling_range, \ +never null. A trim leaves the room at its setpoint, without the reference's cushion, so an occupied room \ +running warm with no trim held gets more cooling than baseline_action: from 0 at {sp.warm_margin_c:g} C \ +above its setpoint to {sp.warm_boost:g} at {sp.warm_margin_c + sp.warm_ramp_c:g} C or more above. \ +Otherwise cooling is null, exactly baseline_action. +2. Battery. Discharge the usable charge evenly over the top-price hours left, keeping {sp.evening_reserve:g} \ +above the floor, each hour no more than load_now_kwh. In any other hour, after the top price and on \ +flat_24h days too, shave when peak.shave_now (district_load_now_kwh at least {sp.shave:g} x \ +forecast_today_kwh): cover the building's own load with whatever charge is left. Charge from the grid only when grid_charging_ok, in the \ +cheapest, cleanest hours, up to {sp.charge_rate:g} of capacity per hour within headroom_kwh, to be near \ +{sp.soc_target:g} when the top price starts. Store spare solar any time. Else idle on flat_24h days. +3. Leave the hot water tank at 0. + +Actions per building, null keeps baseline_action: +- cooling: heat pump power as a fraction of nominal, {cfg.cooling_min:g} to {cfg.cooling_max:g}. +- dhw: hot water tank, fraction of capacity to charge (+) or discharge (-) this hour. Baseline 0. +- battery: fraction of battery capacity to charge (+) or discharge (-) this hour. Baseline 0. +- end_trim: null, or a short reason to end a held trim now (only when end_trim_ok, with null cooling). + +The ranges already apply the envelope's range, rate, state of charge ({cfg.soc_min:g} to \ +{cfg.soc_max:g}), deviation, comfort and trim hold rules. It also clamps the tank by the same rate \ +({cfg.dhw_rate_per_step:g}) and charge limits{spread}{peak}. An invalid reply uses the baseline action \ +everywhere for that hour. Clamps are logged against you. + +Reply with one JSON object only, matching the schema. "reason" is one plain sentence of at most \ +{dc.REASON_TARGET_CHARS} characters. A reason over {dc.REASON_MAX_CHARS} characters makes the whole \ +reply invalid.""" + + def observe(version: int, state: dict, ctx: StepContext, last: Decision | None, look: Lookahead, envelope: Envelope | None = None) -> dict: if version == 1: @@ -1013,6 +1093,8 @@ def observe(version: int, state: dict, ctx: StepContext, last: Decision | None, return build_observation_v7(state, ctx, last, look, envelope) if version == 8: return build_observation_v8(state, ctx, last, look, envelope) + if version == 9: + return build_observation_v9(state, ctx, last, look, envelope) raise ValueError(f"unknown prompt version {version}") @@ -1028,7 +1110,7 @@ def build_request(obs: dict, cfg: EnvelopeConfig, buildings: tuple[str, ...], mo prompt_version: int = 1) -> dict: system = {1: system_prompt, 2: system_prompt_v2, 3: system_prompt_v3, 4: system_prompt_v4, 5: system_prompt_v5, 6: system_prompt_v6, - 7: system_prompt_v7, 8: system_prompt_v8}[prompt_version](cfg, buildings) + 7: system_prompt_v7, 8: system_prompt_v8, 9: system_prompt_v9}[prompt_version](cfg, buildings) return { "model": MODELS[model_key]["id"], "max_tokens": max_tokens, diff --git a/hyperbolic/viewer.py b/hyperbolic/viewer.py index 5a3abe2..94a18e8 100644 --- a/hyperbolic/viewer.py +++ b/hyperbolic/viewer.py @@ -221,9 +221,14 @@ def dumps(data: dict) -> str: return json.dumps(data, sort_keys=True, separators=(",", ":"), ensure_ascii=True) + "\n" -def write_viewer_data(pair: tuple[Path, Path], out_path: Path) -> dict: +def write_viewer_data(pair: tuple[Path, Path], out_path: Path, holdout_final: bool = False) -> dict: """Write viewer.json and, next to it, viewer.js (the same data as a script so the - page also works when opened straight from disk).""" + page also works when opened straight from disk). A pair on the sealed holdout needs + holdout_final.""" + from .months import guard + + guard([json.loads((Path(d) / "manifest.json").read_text(encoding="utf-8")).get("dataset") for d in pair], + holdout_final) data = build_viewer_data(pair) text = dumps(data) out_path.parent.mkdir(parents=True, exist_ok=True) diff --git a/pyproject.toml b/pyproject.toml index ba212a7..f4adf90 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "hyperbolic" -version = "0.10.0" +version = "0.11.0" description = "Supervisory control for CityLearn district simulations, behind a deterministic safety envelope." readme = "README.md" license = { text = "MIT" } diff --git a/scripts/analyze_deviations.py b/scripts/analyze_deviations.py index 7e15dbc..5f840c7 100644 --- a/scripts/analyze_deviations.py +++ b/scripts/analyze_deviations.py @@ -30,6 +30,11 @@ A class's share of the gap is (control - class) / (control - strategy) on each KPI. The classes interact (a trim started earlier changes the rooms later), so the shares need not add up to 1. These are evaluations, never recorded runs. + +From 0.11.0 a prompt version 8 run works the same way against its own strategy: the strategy's +action is the one the observation already shows (strategy, from strategy_plan_v8), class replays +put back strategy_plan_v8's action, and the run is replayed on its own month of the month set. +One class is new for it: warm_skipped, cooling below the strategy's extra cooling in a warm room. """ from __future__ import annotations @@ -49,6 +54,8 @@ from evaluate_hold import KPI_KEYS # noqa: E402 +from hyperbolic import district as dc # noqa: E402 +from hyperbolic import months as mo # noqa: E402 from hyperbolic.envelope import Envelope, EnvelopeConfig # noqa: E402 from hyperbolic.model import ( # noqa: E402 Budget, @@ -73,6 +80,7 @@ "trim_unwanted": ("cooling", "a trim where the strategy has none"), "trim_ended_early": ("cooling", "ended a held trim early with end_trim"), "extra_cooling": ("cooling", "cooling above the strategy's"), + "warm_skipped": ("cooling", "less than the strategy's extra cooling in a warm room"), "top_discharge_short": ("battery", "discharged less than the strategy in a top-price hour"), "top_discharge_over": ("battery", "discharged more than the strategy in a top-price hour"), "shave_skipped": ("battery", "skipped or cut a peak-shaving discharge"), @@ -125,6 +133,8 @@ def classify(obs: dict, reply: dict, strategy: dict | None = None) -> list[tuple out.append((b, "trim_unwanted")) elif hc > sc: out.append((b, "extra_cooling")) + elif sc > base + COOLING_TOL: + out.append((b, "warm_skipped")) hb, sb = _value(mine, "battery", 0.0), _value(plan, "battery", 0.0) if abs(hb - sb) > STORAGE_TOL: mode = battery_mode(obs, b, sb) @@ -168,18 +178,27 @@ def context(obs: dict, b: str) -> dict: } +def strategy_for(obs: dict) -> dict: + """The strategy's action per building for a version 7 or 8 observation: what version 8 shows as + strategy, or what strategy_plan_v7 sends for a version 7 one.""" + first = next(iter(obs["buildings"].values())) + if "strategy" in first: + return {b: s["strategy"] for b, s in obs["buildings"].items()} + if "cooling_range" not in first: + raise ValueError("this needs a prompt version 7 or 8 run") + return strategy_plan_v7(obs) + + def deviations(run_dir: Path) -> list[dict]: - """Every departure in a recorded version 7 run: step, building, class, device, the model's and the - strategy's values and when it happened.""" + """Every departure in a recorded version 7 or 8 run: step, building, class, device, the model's and + the strategy's values and when it happened.""" rows = [] for rec in load_jsonl(run_dir / "calls.jsonl"): text = rec["reply"].get("text") if not text: continue obs = observation_from_request(rec["request"]) - if "cooling_range" not in next(iter(obs["buildings"].values())): - raise ValueError("this needs a prompt version 7 run") - reply, plan = json.loads(text), strategy_plan_v7(obs) + reply, plan = json.loads(text), strategy_for(obs) for b, cls in classify(obs, reply, plan): device = CLASSES[cls][0] base = obs["buildings"][b]["baseline_action"].get(device) or 0.0 @@ -233,17 +252,35 @@ def call(self, step: int, request: dict) -> CallResult: return res +def month_schema_for(m: dict, out: Path) -> Path | None: + """The schema of the month a run's manifest records (None for the phase 2 month, CityLearn's + default). Only the month set: a holdout run is refused.""" + if m.get("dataset", dc.DATASET) == dc.DATASET: + return None + by_dataset = {x.dataset: x for x in mo.months()} + if m["dataset"] not in by_dataset: + raise ValueError(f"dataset {m['dataset']!r} is not in the month set") + return mo.month_schema(by_dataset[m["dataset"]], out) + + def _replay(job: tuple[str, str, list, int]) -> dict: """job: (name, run dir, points to replace, strategy version). control replays the recorded replies; strategy and v8 strategy run that version's scripted strategy on its own.""" + import tempfile + import torch torch.set_num_threads(1) # one simulation per process - from hyperbolic.sim import CityLearnSimulator - name, run, replace, plan = job run_dir = Path(run) m = json.loads((run_dir / "manifest.json").read_text(encoding="utf-8")) + with tempfile.TemporaryDirectory() as tmp: + return _replay_in(name, run_dir, m, replace, plan, month_schema_for(m, Path(tmp))) + + +def _replay_in(name: str, run_dir: Path, m: dict, replace: list, plan: int, schema: Path | None) -> dict: + from hyperbolic.sim import CityLearnSimulator + sup = m["supervisor"] calls = run_dir / "calls.jsonl" version = int(sup["prompt_version"]) @@ -256,24 +293,40 @@ def _replay(job: tuple[str, str, list, int]) -> dict: ctl = AgentController(supervisor=supervisor, envelope=Envelope(EnvelopeConfig.from_manifest(m["envelope"])), budget=Budget(limit_usd=float(sup["budget_usd"])), max_tokens=int(sup["max_tokens"]), effort=sup["effort"], prompt_version=version) - res = run_session(CityLearnSimulator(control=True), ctl, NullWriter(), hours=m.get("hours_limit"), + writer = SeriesWriter() + res = run_session(CityLearnSimulator(control=True, schema=schema), ctl, writer, hours=m.get("hours_limit"), sim_info={"kind": "citylearn", "control": True}, log=lambda *_: None) return {"name": name, "kpis": {k: res["district_kpis"][k] for k in KPI_KEYS}, - "clamped": res["clamped_steps"], "fallback": res["fallback_steps"]} + "clamped": res["clamped_steps"], "fallback": res["fallback_steps"], "grid_kwh": writer.grid_kwh} + + +class SeriesWriter(NullWriter): + """Keeps each hour's grid draw per building, so a replay's peaks can be found.""" + + def __init__(self) -> None: + super().__init__() + self.grid_kwh: dict[str, list[float]] = collections.defaultdict(list) + + def step(self, rec: dict) -> None: + for b, o in rec["outcome"].items(): + self.grid_kwh[b].append(o["grid_kwh"]) def attribute(run_dir: Path, rows: list[dict], jobs: int | None = None, classes: list[str] | None = None, estimates: bool = True) -> dict: - """KPIs for control, each class replaced on its own, all classes, the strategy and, with - estimates, the three prompt version 8 replays.""" + """KPIs for control, each class replaced on its own, all classes, the strategy and, for a version 7 + run with estimates, the three prompt version 8 replays. A version 8 run is measured against the + version 8 strategy.""" + version = int(json.loads((run_dir / "manifest.json").read_text(encoding="utf-8"))["supervisor"]["prompt_version"]) + plan = 8 if version >= 8 else 7 points = collections.defaultdict(set) for r in rows: points[r["class"]].add((r["step"], r["building"], r["device"])) classes = [c for c in (classes or CLASSES) if points[c]] every = sorted(set().union(*(points[c] for c in classes))) if classes else [] - work = [("control", str(run_dir), [], 7), ("strategy", str(run_dir), [], 7)] - work += [(c, str(run_dir), sorted(points[c]), 7) for c in classes] + [("all", str(run_dir), every, 7)] - if estimates: + work = [("control", str(run_dir), [], plan), ("strategy", str(run_dir), [], plan)] + work += [(c, str(run_dir), sorted(points[c]), plan) for c in classes] + [("all", str(run_dir), every, plan)] + if estimates and plan == 7: steps = sorted({r["step"] for r in load_jsonl(run_dir / "calls.jsonl")}) buildings = json.loads((run_dir / "manifest.json").read_text(encoding="utf-8"))["buildings"] work += [("v8 top class", str(run_dir), sorted(points["trim_not_started"]), 8), @@ -309,7 +362,7 @@ def main(argv: list[str] | None = None) -> int: args = ap.parse_args(argv) rows = deviations(args.run_dir) summ = summary(rows) - print(f"{args.run_dir.name}: departures from the version 7 strategy, by class (building-hours)") + print(f"{args.run_dir.name}: departures from its strategy, by class (building-hours)") for cls, s in sorted(summ.items(), key=lambda x: -x[1]["count"]): print(f" {cls:<20} {s['count']:>4} in {s['hours']:>3} hours, mean gap {s['mean_gap']:.4f}: {CLASSES[cls][1]}") for k in ("price", "occupied", "outdoor_c", "soc", "room", "building"): @@ -330,10 +383,11 @@ def main(argv: list[str] | None = None) -> int: print("\nshare of the control to strategy gap explained") for n in order[2:]: print(f" {n:<20}" + "".join(f"{'n/a' if sh[n][k] is None else format(sh[n][k], '.2f'):>15}" for k in SHOW)) - print("\nestimates for prompt version 8: the model's replies with the version 8 strategy filled in") - for n in ("control", "v8 top class", "v8 cooling", "v8 strategy"): - r = results[n] - print(f" {n:<20}" + "".join(f"{r['kpis'][k]:>15.4f}" for k in SHOW) + f"{r['clamped']:>9}") + if "v8 strategy" in results: + print("\nestimates for prompt version 8: the model's replies with the version 8 strategy filled in") + for n in ("control", "v8 top class", "v8 cooling", "v8 strategy"): + r = results[n] + print(f" {n:<20}" + "".join(f"{r['kpis'][k]:>15.4f}" for k in SHOW) + f"{r['clamped']:>9}") if args.out: args.out.write_text(json.dumps({"summary": summ, "results": results, "shares": sh}, indent=2, sort_keys=True), encoding="utf-8") diff --git a/scripts/analyze_peaks.py b/scripts/analyze_peaks.py new file mode 100644 index 0000000..67a45d7 --- /dev/null +++ b/scripts/analyze_peaks.py @@ -0,0 +1,169 @@ +"""Where the peaks of a recorded prompt version 8 run come from, and how much of each the agent's +departures from its strategy explain (0.11.0). + + python scripts/analyze_peaks.py RUN_DIR [RUN_DIR ...] [--top 5] [--jobs N] [--out FILE] + +CityLearn's all_time_peak_average is the district's highest hourly net draw over the episode over +the no-control run's; daily_peak_average is the mean of each day's highest hour over the same for +the no-control run (days are 24 hour blocks from the first hour). So one hour sets the all-time +peak and every day's top hour sets the daily peak. + +Peak hours. For the run and for the scripted version 8 strategy on the same month, the hour of the +all-time peak and of the top daily peaks, district and per building, with what the model sent and +what the strategy would have sent for the very observation the model saw (the observation's own +strategy) in that hour and the three before it, and the deviation classes there +(scripts/analyze_deviations.py, which now reads version 8 runs). + +Attribution. The run's replies are replayed on a fresh episode of the same month through the +unchanged envelope: control (must reproduce the published KPIs), the scripted strategy, every +departure class put back to the strategy on its own, and all at once. A class's share of the gap +on a KPI is (control - class) / (control - strategy). Classes are ranked by the all-time peak, then +the daily peak, then ramping, summed over the runs given. + +These are evaluations, never recorded runs, and run only on the month set (never the holdout). +""" + +from __future__ import annotations + +import argparse +import collections +import json +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "scripts")) + +import analyze_deviations as ad # noqa: E402 + +from hyperbolic.model import observation_from_request # noqa: E402 +from hyperbolic.runner import load_jsonl # noqa: E402 + +PEAK_KPIS = ("all_time_peak_average", "daily_peak_average", "ramping_average") +LOOKBACK = 3 # hours before a peak whose departures are listed with it + + +def district(grid_kwh: dict[str, list[float]]) -> list[float]: + n = min(len(v) for v in grid_kwh.values()) + return [sum(v[t] for v in grid_kwh.values()) for t in range(n)] + + +def peak_hours(grid_kwh: dict[str, list[float]], top: int = 5) -> dict: + """The all-time peak hour and the top daily peak hours, district and per building.""" + net = district(grid_kwh) + days = collections.defaultdict(list) + for t, v in enumerate(net): + days[t // 24].append((v, t)) + daily = sorted((max(h) for h in days.values()), reverse=True) + at = max(range(len(net)), key=lambda t: net[t]) + return { + "all_time": {"step": at, "kwh": net[at], "buildings": {b: v[at] for b, v in grid_kwh.items()}}, + "daily_mean_kwh": sum(v for v, _ in daily) / len(daily), + "top_daily": [{"step": t, "kwh": v, "buildings": {b: x[t] for b, x in grid_kwh.items()}} for v, t in daily[:top]], + "building_peaks": {b: {"step": max(range(len(v)), key=lambda t: v[t]), "kwh": max(v)} for b, v in grid_kwh.items()}, + } + + +def around(run_dir: Path, step: int, lookback: int = LOOKBACK) -> list[dict]: + """What the model sent and what its strategy would have sent, per building, in step and the hours before.""" + calls = {c["step"]: c for c in load_jsonl(run_dir / "calls.jsonl")} + out = [] + for t in range(max(0, step - lookback), step + 1): + rec = calls.get(t) + if rec is None or not rec["reply"].get("text"): + out.append({"step": t, "fallback": True}) + continue + obs, reply = observation_from_request(rec["request"]), json.loads(rec["reply"]["text"]) + plan = ad.strategy_for(obs) + out.append({ + "step": t, "hour": obs["hour"], "top_price_now": obs["price_plan"]["top_price_now"], + "district_load_now_kwh": obs["peak"]["district_load_now_kwh"], + "forecast_today_kwh": obs["peak"]["forecast_today_kwh"], + "buildings": {b: {"model": {d: reply[b].get(d) for d in ("cooling", "battery")}, + "strategy": {d: plan[b].get(d) for d in ("cooling", "battery")}, + "soc": obs["buildings"][b]["battery"]["soc"]} for b in sorted(obs["buildings"])}, + "classes": sorted(f"{b} {c}" for b, c in ad.classify(obs, reply, plan)), + }) + return out + + +def analyze(run_dir: Path, top: int = 5, jobs: int | None = None) -> dict: + rows = ad.deviations(run_dir) + results = ad.attribute(run_dir, rows, jobs) + published = json.loads((run_dir / "result.json").read_text(encoding="utf-8"))["district_kpis"] + if any(results["control"]["kpis"][k] != published[k] for k in ad.KPI_KEYS): + raise AssertionError(f"{run_dir.name}: control does not reproduce the published KPIs") + live, strat = peak_hours(results["control"]["grid_kwh"], top), peak_hours(results["strategy"]["grid_kwh"], top) + return { + "run": run_dir.name, "summary": ad.summary(rows), "shares": ad.shares(results), + "kpis": {n: r["kpis"] for n, r in results.items()}, + "peaks": {"model": live, "strategy": strat}, + "at_peak": {"model_all_time": around(run_dir, live["all_time"]["step"]), + "model_top_daily": [around(run_dir, p["step"], 0)[0] for p in live["top_daily"]]}, + "class_peaks": {n: peak_hours(r["grid_kwh"], 1)["all_time"] for n, r in results.items()}, + } + + +def ranking(analyses: list[dict]) -> list[tuple[str, dict[str, float]]]: + """Classes by how much of the gap they explain, summed over runs: all-time peak, daily peak, ramping. + Each value is the KPI points (control - class), so runs add up.""" + total: dict[str, dict[str, float]] = collections.defaultdict(lambda: dict.fromkeys(PEAK_KPIS, 0.0)) + for a in analyses: + c = a["kpis"]["control"] + for name, k in a["kpis"].items(): + if name in ("control", "strategy", "all"): + continue + for x in PEAK_KPIS: + total[name][x] += c[x] - k[x] + return sorted(total.items(), key=lambda kv: tuple(-kv[1][x] for x in PEAK_KPIS)) + + +def _fmt_hour(a: dict) -> str: + if a.get("fallback"): + return f" step {a['step']}: fallback" + bs = "; ".join(f"{b[-1]} cool {v['model']['cooling']}/{v['strategy']['cooling']} bat {v['model']['battery']}/" + f"{v['strategy']['battery']} soc {v['soc']}" for b, v in a["buildings"].items()) + return (f" step {a['step']} h{a['hour']}{' top' if a['top_price_now'] else ''} load {a['district_load_now_kwh']} " + f"fc {a['forecast_today_kwh']}: {bs} {', '.join(a['classes']) or 'as the strategy'}") + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("run_dirs", type=Path, nargs="+") + ap.add_argument("--top", type=int, default=5, help="top daily peaks to list (default 5)") + ap.add_argument("--jobs", type=int, default=None, help="parallel simulations (default: one per CPU)") + ap.add_argument("--out", type=Path, default=None, help="optionally write every result as JSON") + args = ap.parse_args(argv) + analyses = [] + for d in args.run_dirs: + a = analyze(d, args.top, args.jobs) + analyses.append(a) + k, pm, ps = a["kpis"], a["peaks"]["model"], a["peaks"]["strategy"] + print(f"\n{d.name}: control reproduces the published KPIs exactly") + print(f" {'replay':<22}" + "".join(f"{x[:16]:>18}" for x in PEAK_KPIS) + f"{'peak step':>11}{'peak kWh':>10}") + for n in ["control", "strategy", "all", *sorted(n for n in k if n not in ("control", "strategy", "all"))]: + cp = a["class_peaks"][n] + print(f" {n:<22}" + "".join(f"{k[n][x]:>18.4f}" for x in PEAK_KPIS) + f"{cp['step']:>11}{cp['kwh']:>10.2f}") + for who, p in (("model", pm), ("strategy", ps)): + print(f" {who} all-time peak: step {p['all_time']['step']} {p['all_time']['kwh']:.2f} kWh " + f"({', '.join(f'{b[-1]} {v:.2f}' for b, v in p['all_time']['buildings'].items())}); " + f"mean daily peak {p['daily_mean_kwh']:.2f} kWh; building peaks " + + ", ".join(f"{b[-1]} {v['kwh']:.2f} at {v['step']}" for b, v in p["building_peaks"].items())) + print(" model at its all-time peak (model/strategy):") + for h in a["at_peak"]["model_all_time"]: + print(_fmt_hour(h)) + print(" model at its top daily peaks:") + for h in a["at_peak"]["model_top_daily"]: + print(_fmt_hour(h)) + print("\nclasses ranked by KPI points explained (control - class), summed over the runs") + print(f" {'class':<22}" + "".join(f"{x[:16]:>18}" for x in PEAK_KPIS)) + for name, v in ranking(analyses): + print(f" {name:<22}" + "".join(f"{v[x]:>+18.4f}" for x in PEAK_KPIS)) + if args.out: + args.out.write_text(json.dumps(analyses, indent=1, sort_keys=True), encoding="utf-8") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/split_ramping.py b/scripts/split_ramping.py index 6c776b8..70e9ab1 100644 --- a/scripts/split_ramping.py +++ b/scripts/split_ramping.py @@ -47,12 +47,21 @@ def resimulate(run_dir: Path): - """Run a recorded run's actions through CityLearn again. Returns (CityLearnEnv, steps).""" + """Run a recorded run's actions through CityLearn again, on the month it was recorded on. + Returns (CityLearnEnv, steps).""" + import tempfile + + from hyperbolic import district as dc + from hyperbolic import months as mo from hyperbolic.sim import CityLearnSimulator steps = load_jsonl(Path(run_dir) / "steps.jsonl") m = json.loads((Path(run_dir) / "manifest.json").read_text(encoding="utf-8")) - sim = CityLearnSimulator(control=steps[0]["actions"] is not None) + schema = None + if m.get("dataset", dc.DATASET) != dc.DATASET: # a tuning month (never the holdout) + month = {x.dataset: x for x in mo.months()}[m["dataset"]] + schema = mo.month_schema(month, Path(tempfile.mkdtemp(prefix="hyperbolic_"))) + sim = CityLearnSimulator(control=steps[0]["actions"] is not None, schema=schema) state = sim.start(m.get("hours_limit")) i = 0 while state is not None and i < len(steps): diff --git a/scripts/tune_strategy.py b/scripts/tune_strategy.py index fa2a902..b232b9c 100644 --- a/scripts/tune_strategy.py +++ b/scripts/tune_strategy.py @@ -56,6 +56,18 @@ breaking ties; with none, no extra cooling is shipped. (A first rule that also held cost, emissions, both peaks and ramping within 0.001 of the reference qualified none of the 36: the cheapest full fix costs about 0.002 of cost, and the night rule costs more and clears no warm hour.) + +Version 9 (0.11.0) searches where the 0.10.0 peak diagnosis (scripts/analyze_peaks.py) points: the +all-time peak falls in an evening hour just after the top-price discharge, with the batteries at +their floor, so the grid has charge kept back through the top-price hours for the evening shave +(StrategyParamsV9.evening_reserve), the share of the forecast peak that counts as a peak hour +(shave), and how fast a grid charge may rise (charge_step), with the 0.8.0 rules, unchanged. It runs +on the four months of the month set (the phase 2 month and the three tuning months; never the +holdout) and applies the acceptance rule written into the CHANGELOG before any version 9 result +(accepts_v9): on the mean of the four months, a lower all-time peak and lower ramping than the +shipped version 8 strategy, cost, emissions, discomfort and too warm no more than 0.001 higher, and +unserved energy in outages no higher. The passing candidate with the lowest all-time peak plus +ramping wins, the lower cost breaking ties; with none, nothing new ships. """ from __future__ import annotations @@ -134,6 +146,24 @@ {"warm_margin_c": 1.0, "warm_boost": 0.1, "warm_ramp_c": 1.0}) GRID_V8 = (*GRID_V8_WARM, *GRID_V8_NIGHT) V8_SLACK = 0.001 +# Version 9: everything else at the shipped version 8 numbers. +GRID_V9 = { + "evening_reserve": (0.0, 0.05, 0.1, 0.15, 0.2, 0.25), + "shave": (0.9, 0.8), + "charge_step": (0.15, 0.1, 0.05), +} +# A second, finer pass, added after the first passed none: the reserve's cost grows about 0.003 per +# 0.05, so smaller reserves are the only ones that could stay within 0.001 of cost. +GRID_V9_FINE = { + "evening_reserve": (0.01, 0.02, 0.03, 0.04), + "shave": (0.9,), + "charge_step": (0.15, 0.125), +} +OUTAGE = "power_outage_normalized_unserved_energy_total" +# The acceptance rule (CHANGELOG, 0.11.0), on the mean of the four months against v8 scripted. +V9_IMPROVE = ("all_time_peak_average", "ramping_average") +V9_HOLD = ("cost_total", "carbon_emissions_total", "discomfort_proportion", "discomfort_hot_proportion") +V9_SLACK = 0.001 def month_datasets(out: Path) -> list[Path]: @@ -179,7 +209,7 @@ def kpis(job: tuple[str, dict | None, str]) -> dict: res = run_session(CityLearnSimulator(control=control, schema=Path(schema)), controller, NullWriter(), log=lambda *_: None) k = res["district_kpis"] - return ({key: k.get(key) for key in (*KEYS, *COMFORT)} + return ({key: k.get(key) for key in (*KEYS, *COMFORT, OUTAGE)} | {"clamped": res["clamped_steps"], "fallback": res["fallback_steps"]}) @@ -236,7 +266,7 @@ def line(name: str, k: dict) -> str: def main(argv: list[str] | None = None) -> int: ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - ap.add_argument("--version", type=int, default=5, choices=(4, 5, 6, 7, 8)) + ap.add_argument("--version", type=int, default=5, choices=(4, 5, 6, 7, 8, 9)) ap.add_argument("--quick", action="store_true") ap.add_argument("--jobs", type=int, default=None, help="parallel simulations (default: one per CPU)") ap.add_argument("--out", type=Path, default=None, help="optionally write every result as JSON") @@ -250,6 +280,8 @@ def main(argv: list[str] | None = None) -> int: return main_v7(args) if args.version == 8: return main_v8(args) + if args.version == 9: + return main_v9(args) with tempfile.TemporaryDirectory() as tmp: months = [str(d / "schema.json") for d in month_datasets(Path(tmp))] candidates = [asdict(StrategyParams())] if args.quick else [ @@ -445,5 +477,73 @@ def main_v8(args) -> int: return 0 +def grid_v9() -> list[dict]: + """The version 9 grid around the shipped version 8 numbers, then the finer second pass; the first + candidate is version 8 itself.""" + v8 = asdict(StrategyParamsV8()) + return [{**v8, **dict(zip(g, combo))} for g in (GRID_V9, GRID_V9_FINE) for combo in itertools.product(*g.values())] + + +def accepts_v9(k: dict, ref: dict) -> bool: + """The 0.11.0 acceptance rule on four-month means: all-time peak and ramping below ref's; cost, + emissions, discomfort and too warm no more than V9_SLACK above ref's; outage energy not above it.""" + return (all(k[x] < ref[x] for x in V9_IMPROVE) + and all(k[x] <= ref[x] + V9_SLACK for x in V9_HOLD) + and k[OUTAGE] <= ref[OUTAGE]) + + +def pick_v9(rows: list[dict]) -> dict | None: + """The passing candidate with the lowest all-time peak plus ramping, the lower cost breaking ties.""" + passing = [r for r in rows if r["accepted"]] + if not passing: + return None + return min(passing, key=lambda r: (r["kpis"]["all_time_peak_average"] + r["kpis"]["ramping_average"], + r["kpis"]["cost_total"], json.dumps(r["params"], sort_keys=True))) + + +def mean_v9(rows: list[dict]) -> dict: + """Four-month means at CityLearn's six decimals, as hyperbolic months reports them.""" + return {k: sum(r[k] for r in rows) / len(rows) for k in (*KEYS, *COMFORT, OUTAGE, "clamped")} + + +def line_v9(name: str, k: dict) -> str: + return line_v8(name, k) + f" outage {k[OUTAGE]:.4f}" + + +def main_v9(args) -> int: + """Charge kept for the evening shave, the shave share and the charge rise on the four months of the + month set, against the shipped version 8 strategy, under the 0.11.0 acceptance rule.""" + with tempfile.TemporaryDirectory() as tmp: + months = [str(mo.month_schema(m, Path(tmp))) for m in mo.months()] + candidates = [asdict(StrategyParamsV8())] if args.quick else grid_v9() + jobs = [("baseline", None, m) for m in months] + [("strategy8", asdict(StrategyParamsV8()), m) for m in months] + jobs += [("strategy8", c, m) for c in candidates for m in months] + with cf.ProcessPoolExecutor(max_workers=args.jobs, mp_context=mp.get_context("spawn")) as pool: + results = list(pool.map(kpis, jobs)) + n = len(months) + base, ref = mean_v9(results[:n]), mean_v9(results[n:2 * n]) + print(f"month set: {', '.join(m.key for m in mo.months())}, mean over {n} months") + print(line_v9("baseline", base)) + print(line_v9("v8 shipped", ref)) + rows = [] + for i, c in enumerate(candidates): + got = results[2 * n + i * n:2 * n + (i + 1) * n] + k = mean_v9(got) + rows.append({"params": c, "kpis": k, "months": got, "accepted": accepts_v9(k, ref)}) + print(line_v9(" ".join(f"{g}={c[g]}" for g in GRID_V9), k) + (" passes" if rows[-1]["accepted"] else "")) + best = pick_v9(rows) + print(f"{sum(r['accepted'] for r in rows)} of {len(rows)} candidates pass the acceptance rule") + if best is None: + print("none passes: nothing new ships, version 8 stays") + else: + print(line_v9("best", best["kpis"])) + v8 = asdict(StrategyParamsV8()) + print(f"best settings differ from version 8 in: {({k: v for k, v in best['params'].items() if v != v8[k]})}") + if args.out: + args.out.write_text(json.dumps({"baseline": base, "v8": ref, "rows": rows, "best": best}, + indent=2, sort_keys=True), encoding="utf-8") + return 0 + + if __name__ == "__main__": raise SystemExit(main()) diff --git a/tests/test_holdout.py b/tests/test_holdout.py new file mode 100644 index 0000000..9db75d7 --- /dev/null +++ b/tests/test_holdout.py @@ -0,0 +1,142 @@ +"""The sealed holdout (0.11.0, HOLDOUT.md): Buildings 4, 5 and 6 of citylearn_challenge_2023_phase_3_1, kept out +of the month set, scored only with --holdout-final and only on files that match the seal. Nothing here runs a +controller on it; the one CityLearn test is the baseline load check.""" + +import json +import re +import shutil +from pathlib import Path + +import pytest +from conftest import needs_citylearn + +from hyperbolic import cli +from hyperbolic import months as mo +from hyperbolic.viewer import write_viewer_data + +ROOT = Path(__file__).resolve().parents[1] + + +def test_the_holdout_is_three_months_of_buildings_4_to_6_and_never_in_the_month_set(): + assert [m.key for m in mo.months()] == ["phase2", "tuning-06", "tuning-07", "tuning-08"] + hs = mo.holdout_months() + assert [m.key for m in hs] == ["holdout-06", "holdout-07", "holdout-08"] + assert [mo.hours(m) for m in hs] == [719, 743, 743] + assert all(m.holdout and m.buildings == ("Building_4", "Building_5", "Building_6") for m in hs) + assert hs[1].dataset == "citylearn_challenge_2023_phase_3_1_buildings_4_5_6_month_07" + assert mo.month("holdout-08") == hs[2] and mo.is_holdout("holdout-06") and mo.is_holdout(hs[0].dataset) + assert not any(mo.is_holdout(m.key) or mo.is_holdout(m.dataset) for m in mo.months()) + + +def test_a_holdout_month_is_written_with_only_its_own_buildings(tmp_path): + schema = mo.month_schema(mo.month("holdout-07"), tmp_path) + data = json.loads(schema.read_text(encoding="utf-8")) + assert sorted(data["buildings"]) == ["Building_4", "Building_5", "Building_6"] + assert data["simulation_start_time_step"] == 0 and data["simulation_end_time_step"] == 743 + assert sorted(p.name for p in schema.parent.iterdir()) == [ + "Building_4.csv", "Building_4.pth", "Building_5.csv", "Building_5.pth", "Building_6.csv", "Building_6.pth", + "carbon_intensity.csv", "pricing.csv", "schema.json", "weather.csv"] + lines = (schema.parent / "Building_5.csv").read_text(encoding="utf-8").splitlines() + assert len(lines) == 745 + + +def test_the_seal_matches_the_files_and_holdout_md(): + mo.check_sealed() + text = (ROOT / "HOLDOUT.md").read_text(encoding="utf-8") + table = dict(re.findall(r"^\| (\S+\.(?:csv|pth|json)) \| ([0-9a-f]{64}) \|$", text, re.M)) + assert table == mo.HOLDOUT_SHA256 + + +def test_a_changed_or_extra_holdout_file_breaks_the_seal(tmp_path): + copy = tmp_path / "h" + shutil.copytree(mo.HOLDOUT_DATASET, copy) + mo.check_sealed(copy) + (copy / "notes.txt").write_text("x", encoding="utf-8") + with pytest.raises(mo.HoldoutSealed, match="notes.txt"): + mo.check_sealed(copy) + (copy / "notes.txt").unlink() + p = copy / "pricing.csv" + p.write_bytes(p.read_bytes().replace(b"0.", b"1.", 1)) + with pytest.raises(mo.HoldoutSealed, match="pricing.csv"): + mo.check_sealed(copy) + + +def test_guard_refuses_the_holdout_unless_final(): + mo.guard(["phase2", "tuning-07", mo.month("tuning-06").dataset]) + with pytest.raises(mo.HoldoutSealed, match="--holdout-final"): + mo.guard(["phase2", "holdout-06"]) + with pytest.raises(mo.HoldoutSealed): + mo.guard([mo.month("holdout-08").dataset]) + mo.guard(["holdout-06"], final=True) + + +def test_run_month_refuses_a_holdout_month_before_simulating(tmp_path): + with pytest.raises(mo.HoldoutSealed): + mo.run_month(("baseline", "holdout-06", str(tmp_path))) + assert not any(tmp_path.iterdir()) + + +def _fake_run(path: Path, dataset: str, mode: str = "agent") -> Path: + path.mkdir() + (path / "manifest.json").write_text(json.dumps({"dataset": dataset, "mode": mode, + "supervisor": {"kind": "live", "model_key": "haiku", + "prompt_version": 9}}), encoding="utf-8") + (path / "result.json").write_text(json.dumps({"steps": 719, "complete": True, "clamped_steps": 0, + "fallback_steps": 0, "cost_usd": 0.4, + "district_kpis": {"cost_total": 0.9}}), encoding="utf-8") + return path + + +def test_recorded_run_on_the_holdout_needs_final(tmp_path): + run = _fake_run(tmp_path / "r", mo.month("holdout-07").dataset) + with pytest.raises(mo.HoldoutSealed): + mo.recorded_run(run) + assert mo.recorded_run(run, final=True)["month"] == "holdout-07" + + +def test_months_cli_refuses_the_holdout_without_the_flag_and_mixed_with_the_month_set(tmp_path, capsys): + assert cli.main(["months", "--months", "holdout-06", "--controllers", "baseline"]) == 2 + assert "sealed holdout" in capsys.readouterr().err + assert cli.main(["months", "--holdout-final", "--controllers", "baseline"]) == 2 + assert cli.main(["months", "--holdout-final", "--months", "holdout-06", "phase2"]) == 2 + run = _fake_run(tmp_path / "r", mo.month("holdout-06").dataset) + assert cli.main(["months", "--months", "phase2", "--controllers", "baseline", "--run", str(run)]) == 2 + p = cli.build_parser() + assert p.parse_args(["months", "--holdout-final", "--months", "holdout-07"]).holdout_final + assert p.parse_args(["compare", "--holdout-final"]).holdout_final + assert not p.parse_args(["months"]).holdout_final + + +def test_compare_refuses_a_holdout_pair_and_writes_nothing(tmp_path, capsys): + ds = mo.month("holdout-06").dataset + base, agent = _fake_run(tmp_path / "b", ds, "baseline"), _fake_run(tmp_path / "a", ds) + out = tmp_path / "data" / "viewer.json" + with pytest.raises(mo.HoldoutSealed): + write_viewer_data((base, agent), out) + assert cli.main(["compare", "--pair", str(base), str(agent), "--out", str(out)]) == 2 + assert "sealed holdout" in capsys.readouterr().err and not out.exists() + + +def test_run_on_the_holdout_records_but_prints_no_kpis(tmp_path, monkeypatch, capsys): + class Sim: + def check_hours(self, hours): + pass + + res = {"steps": 719, "complete": True, "calls": 719, "fallback_steps": 0, "clamped_steps": 3, "cost_usd": 0.41, + "budget_stopped": False, "district_kpis": {"cost_total": 0.9123, "carbon_emissions_total": 0.95, + "discomfort_proportion": 0.05}} + monkeypatch.setattr(cli, "_sim", lambda control, schema=None: Sim()) + monkeypatch.setattr(cli, "run_session", lambda *a, **k: res) + assert cli.main(["run", "--scripted", "--month", "holdout-06", "--runs-dir", str(tmp_path)]) == 0 + out = capsys.readouterr().out + assert "KPIs are not shown" in out and "0.9123" not in out and "cost_total" not in out + + +@needs_citylearn +def test_the_baseline_loads_and_runs_on_the_holdout(tmp_path): + """The load check HOLDOUT.md describes: the no-control reference on holdout June, Buildings 4 to 6 only.""" + assert cli.main(["baseline", "--month", "holdout-06", "--hours", "24", "--runs-dir", str(tmp_path)]) == 0 + (run,) = tmp_path.iterdir() + m = json.loads((run / "manifest.json").read_text(encoding="utf-8")) + assert m["dataset"] == mo.month("holdout-06").dataset and tuple(m["buildings"]) == mo.HOLDOUT_BUILDINGS + assert m["episode_hours"] == 719 diff --git a/tests/test_months.py b/tests/test_months.py index 77fcac0..e9fcf8a 100644 --- a/tests/test_months.py +++ b/tests/test_months.py @@ -90,7 +90,7 @@ def test_cli_takes_a_month_for_baseline_and_run_and_has_the_months_report(): assert p.parse_args(["baseline"]).month == "phase2" a = p.parse_args(["months", "--controllers", "rule", "v8", "--months", "phase2"]) assert a.controllers == ["rule", "v8"] and a.months == ["phase2"] - assert p.parse_args(["months"]).controllers == ["baseline", "rule", "v7", "v8"] + assert p.parse_args(["months"]).controllers == ["baseline", "rule", "v7", "v8", "v9"] with pytest.raises(SystemExit): p.parse_args(["run", "--month", "march"]) diff --git a/tests/test_peaks.py b/tests/test_peaks.py new file mode 100644 index 0000000..2ef0476 --- /dev/null +++ b/tests/test_peaks.py @@ -0,0 +1,190 @@ +"""The 0.11.0 peak diagnosis (scripts/analyze_peaks.py, scripts/analyze_deviations.py on version 8 runs) and the +version 9 acceptance rule (scripts/tune_strategy.py).""" + +import json +import sys +from dataclasses import asdict +from pathlib import Path + +import pytest +from conftest import BUILDINGS, needs_citylearn +from test_prompt_v8 import _obs, _warm + +sys.path.insert(0, str(Path(__file__).parents[1] / "scripts")) + +import analyze_deviations as ad # noqa: E402 +import analyze_peaks as ap # noqa: E402 +import tune_strategy as ts # noqa: E402 + +from hyperbolic import months as mo # noqa: E402 +from hyperbolic.envelope import Envelope, EnvelopeConfig # noqa: E402 +from hyperbolic.model import ( # noqa: E402 + Budget, + CallResult, + ScriptedSupervisor, + StrategyParamsV8, + StrategyParamsV9, + observation_from_request, + strategy_plan_v8, +) +from hyperbolic.prompt import build_request # noqa: E402 +from hyperbolic.runner import AgentController, RunWriter, run_session # noqa: E402 + +# -- peak hours ------------------------------------------------------------------------ + + +def test_peak_hours_find_the_all_time_and_daily_peaks_district_and_per_building(): + a = [1.0] * 48 + b = [0.5] * 48 + a[20], b[20] = 6.0, 1.0 # day 1's peak, and the all-time peak (7.0) + a[30], b[33] = 4.0, 5.5 # day 2: the district peaks at 33 (6.5); Building_1 alone at 30 + got = ap.peak_hours({"Building_1": a, "Building_2": b}, top=2) + assert got["all_time"] == {"step": 20, "kwh": 7.0, "buildings": {"Building_1": 6.0, "Building_2": 1.0}} + assert [p["step"] for p in got["top_daily"]] == [20, 33] + assert got["daily_mean_kwh"] == pytest.approx((7.0 + 6.5) / 2) + assert got["building_peaks"] == {"Building_1": {"step": 20, "kwh": 6.0}, "Building_2": {"step": 33, "kwh": 5.5}} + + +def test_ranking_orders_classes_by_all_time_peak_then_daily_peak_then_ramping(): + def k(at, daily, ramp): + return {"all_time_peak_average": at, "daily_peak_average": daily, "ramping_average": ramp} + + runs = [{"kpis": {"control": k(0.90, 0.89, 1.0), "strategy": k(0.85, 0.88, 0.99), "all": k(0.85, 0.88, 0.99), + "shave_skipped": k(0.86, 0.885, 0.995), "charge_skipped": k(0.90, 0.889, 0.98), + "trim_not_started": k(0.90, 0.887, 1.01)}}, + {"kpis": {"control": k(0.95, 0.90, 1.0), "strategy": k(0.95, 0.90, 1.0), + "shave_skipped": k(0.95, 0.899, 1.0), "charge_skipped": k(0.95, 0.90, 0.99), + "trim_not_started": k(0.95, 0.90, 1.0)}}] + got = ap.ranking(runs) + assert [n for n, _ in got] == ["shave_skipped", "trim_not_started", "charge_skipped"] + assert got[0][1]["all_time_peak_average"] == pytest.approx(0.04) + assert got[0][1]["daily_peak_average"] == pytest.approx(0.006) + assert got[2][1]["ramping_average"] == pytest.approx(0.03) + + +# -- deviation classes on version 8 runs ----------------------------------------------------- + + +def test_a_v8_observation_is_measured_against_its_own_strategy(): + *_, obs = _obs(version=8) + assert ad.strategy_for(obs) == {b: obs["buildings"][b]["strategy"] for b in BUILDINGS} + assert ad.classify(obs, json.loads(json.dumps(strategy_plan_v8(obs))), ad.strategy_for(obs)) == [] + *_, v6 = _obs(version=6) + with pytest.raises(ValueError): + ad.strategy_for(v6) + + +def test_cooling_below_the_strategys_warm_room_boost_is_warm_skipped(): + *_, obs = _obs(version=8) + _warm(obs, gap=3.0) + plan = strategy_plan_v8(obs) + assert plan["Building_1"]["cooling"] > 0.3 + ad.COOLING_TOL + reply = json.loads(json.dumps(plan)) + reply["Building_1"]["cooling"] = None + assert ad.classify(obs, reply, plan) == [("Building_1", "warm_skipped")] + reply["Building_1"]["cooling"] = plan["Building_1"]["cooling"] + 0.02 + assert ad.classify(obs, reply, plan) == [("Building_1", "extra_cooling")] + + +def test_replays_run_on_the_month_the_run_was_recorded_on_and_never_the_holdout(tmp_path): + assert ad.month_schema_for({"dataset": mo.PHASE2 and "citylearn_challenge_2023_phase_2_local_evaluation"}, + tmp_path) is None + got = ad.month_schema_for({"dataset": mo.month("tuning-07").dataset}, tmp_path) + assert got.name == "schema.json" and got.parent.name == "tuning-07" + with pytest.raises(ValueError): + ad.month_schema_for({"dataset": mo.month("holdout-07").dataset}, tmp_path) + + +def test_around_shows_the_model_and_the_strategy_with_the_classes(tmp_path): + *_, env, obs = _obs(version=8) + req = build_request(obs, env.config, BUILDINGS, "haiku", prompt_version=8) + plan = strategy_plan_v8(observation_from_request(req)) + mine = json.loads(json.dumps(plan)) + mine["Building_2"]["battery"] = round(plan["Building_2"]["battery"] + 0.1, 3) + with (tmp_path / "calls.jsonl").open("w", encoding="utf-8") as f: + for step, reply in ((3, plan), (4, mine)): + f.write(json.dumps({"step": step, "request": req, "reply": {"text": json.dumps(reply)}}) + "\n") + got = ap.around(tmp_path, 4, lookback=1) + assert [h["step"] for h in got] == [3, 4] and got[0]["classes"] == [] + assert got[1]["buildings"]["Building_2"]["model"]["battery"] == mine["Building_2"]["battery"] + assert got[1]["buildings"]["Building_2"]["strategy"]["battery"] == plan["Building_2"]["battery"] + assert len(got[1]["classes"]) == 1 and got[1]["classes"][0].startswith("Building_2 ") + + +class _NoShave(ScriptedSupervisor): + """The version 8 strategy, except that it never discharges outside a top-price hour.""" + + def call(self, step: int, request: dict) -> CallResult: + obs = observation_from_request(request) + reply = strategy_plan_v8(obs) + if not obs["price_plan"]["top_price_now"]: + for b in reply: + if b != "reason" and reply[b]["battery"] < 0: + reply[b]["battery"] = 0.0 + return CallResult(json.dumps(reply), stop_reason="end_turn", model="scripted") + + +@needs_citylearn +def test_peak_diagnosis_on_a_recorded_citylearn_run(tmp_path): + """A version 8 run on tuning July that never shaves: its departures are skipped shaves, the control + replay reproduces it, its series gives the peak hours, and putting the class back changes the month.""" + from hyperbolic.sim import CityLearnSimulator + + run = tmp_path / "noshave" + m = mo.month("tuning-07") + ctl = AgentController(_NoShave(), Envelope(EnvelopeConfig()), Budget(2.0), prompt_version=8) + run_session(CityLearnSimulator(control=True, schema=mo.month_schema(m, tmp_path / "data")), ctl, RunWriter(run), + hours=95, log=lambda *_: None, sim_info={"kind": "citylearn", "control": True}, dataset=m.dataset) + rows = ad.deviations(run) + assert rows and {r["class"] for r in rows} <= {"shave_skipped", "discharge_off_plan"} + published = json.loads((run / "result.json").read_text(encoding="utf-8"))["district_kpis"] + control = ad._replay(("control", str(run), [], 8)) + assert all(control["kpis"][k] == published[k] for k in ad.KPI_KEYS) + peaks = ap.peak_hours(control["grid_kwh"]) + assert len(control["grid_kwh"]["Building_1"]) == 95 and peaks["all_time"]["kwh"] == max(ap.district(control["grid_kwh"])) + points = sorted({(r["step"], r["building"], r["device"]) for r in rows if r["class"] == "shave_skipped"}) + fixed = ad._replay(("shave_skipped", str(run), points, 8)) + assert fixed["kpis"] != control["kpis"] + + +# -- the version 9 acceptance rule ------------------------------------------------------------- + + +def _k(**over): + base = {"cost_total": 0.9087, "carbon_emissions_total": 0.9582, "discomfort_proportion": 0.0208, + "discomfort_hot_proportion": 0.0059, "all_time_peak_average": 0.8797, "ramping_average": 0.9873, + ts.OUTAGE: 0.1711} + return base | over + + +@pytest.mark.parametrize("over, ok", [ + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870}, True), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, "cost_total": 0.9087 + 0.001}, True), + ({"all_time_peak_average": 0.8797, "ramping_average": 0.9870}, False), # peak not lower + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9873}, False), # ramping not lower + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, "cost_total": 0.9098}, False), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, "carbon_emissions_total": 0.9593}, False), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, "discomfort_proportion": 0.0219}, False), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, "discomfort_hot_proportion": 0.0070}, False), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, ts.OUTAGE: 0.171101}, False), + ({"all_time_peak_average": 0.8790, "ramping_average": 0.9870, ts.OUTAGE: 0.1700}, True), +]) +def test_acceptance_rule_as_written_in_the_changelog(over, ok): + assert ts.accepts_v9(_k(**over), _k()) is ok + + +def test_pick_takes_the_lowest_peak_plus_ramping_then_the_lower_cost_and_none_without_a_pass(): + rows = [{"params": {"n": 1}, "accepted": True, "kpis": _k(all_time_peak_average=0.87, ramping_average=0.985)}, + {"params": {"n": 2}, "accepted": True, "kpis": _k(all_time_peak_average=0.86, ramping_average=0.99, + cost_total=0.9090)}, + {"params": {"n": 3}, "accepted": True, "kpis": _k(all_time_peak_average=0.86, ramping_average=0.99)}, + {"params": {"n": 4}, "accepted": False, "kpis": _k(all_time_peak_average=0.80, ramping_average=0.90)}] + assert ts.pick_v9(rows)["params"] == {"n": 3} + assert ts.pick_v9([dict(r, accepted=False) for r in rows]) is None + + +def test_the_v9_grid_starts_from_v8_and_the_shipped_numbers_are_in_it(): + grid = ts.grid_v9() + assert len(grid) == 44 and grid[0] == asdict(StrategyParamsV8()) + assert asdict(StrategyParamsV9()) in grid + assert all(set(c) == set(asdict(StrategyParamsV8())) for c in grid) diff --git a/tests/test_prompt.py b/tests/test_prompt.py index a5a8839..90f793b 100644 --- a/tests/test_prompt.py +++ b/tests/test_prompt.py @@ -31,13 +31,13 @@ def fake_states(n): return out -def test_default_prompt_is_version_8_and_the_rule_based_controller_stays_on_3(): - assert PROMPT_VERSION == 8 - assert AgentController(ScriptedSupervisor(), Envelope(), Budget(1.0)).describe()["prompt_version"] == 8 +def test_default_prompt_is_version_9_and_the_rule_based_controller_stays_on_3(): + assert PROMPT_VERSION == 9 + assert AgentController(ScriptedSupervisor(), Envelope(), Budget(1.0)).describe()["prompt_version"] == 9 # The rule-based controller is the frozen 0.4.0 reference: prompt version 3, no spread check. assert controller_settings(True) == (3, EnvelopeConfig.v040()) - assert controller_settings(False) == (8, EnvelopeConfig()) - # Versions before 6 keep the 0.7.0 rules; versions 6 to 8 run the 0.8.0 rules with the trim hold. + assert controller_settings(False) == (9, EnvelopeConfig()) + # Versions before 6 keep the 0.7.0 rules; versions 6 to 9 run the 0.8.0 rules with the trim hold. assert controller_settings(True, 4) == (4, EnvelopeConfig.v070()) assert controller_settings(True, 5) == (5, EnvelopeConfig.v070()) assert controller_settings(False, 5) == (5, EnvelopeConfig.v070()) @@ -125,12 +125,12 @@ def test_v2_prompt_states_the_strategy_and_the_rules(): def test_request_carries_the_observation_for_each_version(): st = fake_states(1)[0] ctx = context_from_state(st) - for version in (1, 2, 3, 4, 5, 6, 7, 8): + for version in (1, 2, 3, 4, 5, 6, 7, 8, 9): obs = observe(version, st, ctx, None, Lookahead()) req = build_request(obs, EnvelopeConfig(), tuple(st["buildings"]), "haiku", prompt_version=version) assert observation_from_request(req) == json.loads(json.dumps(obs)) with pytest.raises(ValueError): - observe(9, st, ctx, None, Lookahead()) + observe(10, st, ctx, None, Lookahead()) @pytest.mark.parametrize("version", [3, 4, 5, 6, 7]) diff --git a/tests/test_prompt_v8.py b/tests/test_prompt_v8.py index c179c26..bb6173a 100644 --- a/tests/test_prompt_v8.py +++ b/tests/test_prompt_v8.py @@ -57,8 +57,8 @@ def _warm(obs, b="Building_1", gap=2.0, **over): # -- what each building sees ---------------------------------------------------------- -def test_v8_is_the_default_and_v7_stays_selectable(): - assert PROMPT_VERSION == 8 and PROMPT_VERSIONS[-2:] == (7, 8) +def test_v7_and_v8_stay_selectable(): + assert PROMPT_VERSION == 9 and PROMPT_VERSIONS[-3:] == (7, 8, 9) st, ctx, look, env, v8 = _obs() v7 = observe(7, st, ctx, None, look, env) assert "strategy_battery" in v7["buildings"]["Building_1"] and "signal_outlook" in v7 diff --git a/tests/test_prompt_v9.py b/tests/test_prompt_v9.py new file mode 100644 index 0000000..807e915 --- /dev/null +++ b/tests/test_prompt_v9.py @@ -0,0 +1,151 @@ +"""Prompt version 9 (0.11.0): charge kept for the evening shave, peak.shave_now, the wording on small numbers +and shaving after the top price, version 8 still selectable, and the 0.10.0 live months unchanged.""" + +import json +from dataclasses import asdict +from pathlib import Path + +import pytest +from conftest import BUILDINGS, needs_citylearn +from fakes import FakeDistrict +from test_prompt_v8 import _obs + +from hyperbolic.envelope import Envelope, EnvelopeConfig, parse_reply +from hyperbolic.model import ( + Budget, + ScriptedSupervisor, + StrategyParamsV8, + StrategyParamsV9, + strategy_plan_v8, + strategy_plan_v9, +) +from hyperbolic.prompt import PROMPT_VERSION, build_request, observe, system_prompt_v8, system_prompt_v9 +from hyperbolic.runner import AgentController, RunWriter, load_jsonl, replay_run, run_session + +LIVE = Path(__file__).parents[1] / "runs" / "live" +LIVE_0100 = {"20261023T160151Z": 719, "20261023T160150Z": 719, "20261023T160200Z": 743, "20261023T160206Z": 743} + + +def _battery_hour(obs, top=True, load_now=5.0, forecast=10.0, soc=0.3): + """Every building with a big load, room in its range and soc of charge, in the last top-price hour or not.""" + obs["price_plan"].update({"top_price_now": top, "flat_24h": False, "top_price_hours_ahead": []}) + obs["peak"].update({"district_load_now_kwh": load_now, "forecast_today_kwh": forecast}) + for b in BUILDINGS: + s = obs["buildings"][b] + s["load_now_kwh"] = 5.0 + s["battery"].update({"soc": soc, "soc_floor": 0.2, "max_fraction_per_hour": 1.0, "capacity_kwh": 4.0}) + s["battery_range"] = [-0.5, 0.5] + s["last_action"] = {"cooling": 0.3, "dhw": 0.0, "battery": 0.0} + return obs + + +def test_v9_is_the_default_and_only_adds_shave_now_to_the_v8_observation(): + assert PROMPT_VERSION == 9 + st, ctx, look, env, v9 = _obs(version=9) + v8 = observe(8, st, ctx, None, look, env) + assert set(v9) == set(v8) and set(v9["peak"]) == set(v8["peak"]) | {"shave_now"} + assert "shave_now" not in v8["peak"] # version 8 requests stay byte for byte, so 0.10.0 runs replay + for b in BUILDINGS: + assert set(v9["buildings"][b]) == set(v8["buildings"][b]) + plan = strategy_plan_v9(v9) + assert v9["strategy_reason"] == plan["reason"] + for b in BUILDINGS: + assert v9["buildings"][b]["strategy"] == {d: plan[b][d] for d in ("cooling", "dhw", "battery")} + + +def test_shave_now_is_the_strategys_own_peak_hour_test(): + sp = StrategyParamsV9() + st, ctx, look, env, v9 = _obs(version=9) + pk = v9["peak"] + want = bool(pk["district_load_now_kwh"] is not None and pk["forecast_today_kwh"] + and pk["district_load_now_kwh"] >= sp.shave * pk["forecast_today_kwh"]) + assert pk["shave_now"] is want + + +def test_v9_strategy_is_v8_with_charge_kept_through_the_top_price_hours(): + v8, v9 = asdict(StrategyParamsV8()), asdict(StrategyParamsV9()) + assert {k for k in v8 if v8[k] != v9[k]} == {"evening_reserve"} and v9["evening_reserve"] == 0.01 + *_, obs = _obs(version=9) + top = _battery_hour(json.loads(json.dumps(obs)), top=True, soc=0.3) + a, b = strategy_plan_v8(top)["Building_1"]["battery"], strategy_plan_v9(top)["Building_1"]["battery"] + # The usable charge above the floor is 0.099; version 9 keeps 0.01 of it (both truncate to 3 decimals). + assert a == pytest.approx(-0.099, abs=1.5e-3) and b - a == pytest.approx(0.01, abs=1.5e-3) + # After the top price, in a peak hour, the charge left goes to the shave (0.243: tuning July's evening peak). + shave = _battery_hour(json.loads(json.dumps(obs)), top=False, load_now=12.0, forecast=10.0, soc=0.243) + assert strategy_plan_v9(shave)["Building_1"]["battery"] < 0 and "shave" in strategy_plan_v9(shave)["reason"].lower() + + +def test_v9_system_prompt_says_copy_small_numbers_and_shave_after_the_top_price(): + cfg = EnvelopeConfig() + text, old = system_prompt_v9(cfg, BUILDINGS), system_prompt_v8(cfg, BUILDINGS) + sp = StrategyParamsV9() + for phrase in ("Send it as given, every number exactly", "battery -0.033 is not 0", + "cooling under baseline_action is not null, trim state off", "after the top price and on", + "flat_24h days too, shave when", "peak.shave_now", f"keeping {sp.evening_reserve:g} above the floor", + "daily and monthly peak", "whatever charge is left", "That starts a trim when state is off", + "never null"): + assert phrase in " ".join(text.split()), phrase + # The version 8 lines that read as "no shaving after the top price or on a flat day" are gone. + assert "Before them discharge only to shave" in old and "Before them" not in text + assert "Idle on flat_24h days." in old and "Idle on flat_24h days." not in text + assert chr(0x2014) not in text and "local evaluation" not in text and "strategy_battery" not in text + + +def test_v9_request_is_within_3_percent_of_v8_with_the_same_reply_schema(): + st, ctx, look, env, v9 = _obs(version=9) + v8 = observe(8, st, ctx, None, look, env) + r8 = build_request(v8, env.config, BUILDINGS, "haiku", prompt_version=8) + r9 = build_request(v9, env.config, BUILDINGS, "haiku", prompt_version=9) + assert r9["output_config"] == r8["output_config"] + assert len(json.dumps(r8)) < len(json.dumps(r9)) < 1.03 * len(json.dumps(r8)) + + +def test_scripted_replies_follow_their_own_versions_strategy(): + st, ctx, look, env, v9 = _obs(version=9) + req = build_request(v9, env.config, BUILDINGS, "haiku", prompt_version=9) + p, err = parse_reply(ScriptedSupervisor().call(5, req).text, BUILDINGS, end_trim=True) + assert err is None + for b in BUILDINGS: + assert p["buildings"][b] == v9["buildings"][b]["strategy"] + v8 = observe(8, st, ctx, None, look, env) + got = json.loads(ScriptedSupervisor().call(5, build_request(v8, env.config, BUILDINGS, "haiku", + prompt_version=8)).text) + assert got == json.loads(json.dumps(strategy_plan_v8(v8))) + + +def test_v9_run_on_the_fake_district_is_clean_and_replays(tmp_path): + run = tmp_path / "v9" + ctl = AgentController(ScriptedSupervisor(), Envelope(EnvelopeConfig()), Budget(2.0), prompt_version=9) + res = run_session(FakeDistrict(), ctl, RunWriter(run), hours=48, log=lambda *_: None) + assert res["fallback_steps"] == 0 and res["clamped_steps"] == 0 and ctl.describe()["prompt_version"] == 9 + rep = replay_run(run) + assert rep.deterministic and rep.identical_steps == 48 + + +@needs_citylearn +def test_v9_dry_run_and_replay(tmp_path): + """24 hours of the scripted version 9 strategy on CityLearn, through the unchanged rules, and replay.""" + from hyperbolic.runner import controller_settings + from hyperbolic.sim import CityLearnSimulator + + version, cfg = controller_settings(rule_based=True, prompt_version=9) + assert cfg == EnvelopeConfig() + ctl = AgentController(ScriptedSupervisor(), Envelope(cfg), Budget(2.0), prompt_version=version) + run_dir = tmp_path / "v9" + res = run_session(CityLearnSimulator(control=True), ctl, RunWriter(run_dir), hours=24, log=lambda *_: None, + sim_info={"kind": "citylearn", "control": True}) + assert res["steps"] == 24 and res["fallback_steps"] == 0 + assert '"shave_now":' in load_jsonl(run_dir / "calls.jsonl")[5]["request"]["messages"][0]["content"] + rep = replay_run(run_dir) + assert rep.deterministic and rep.api_calls == 0 and rep.identical_steps == 24 + + +@pytest.mark.parametrize("stamp, hours", sorted(LIVE_0100.items())) +def test_the_0_10_0_live_months_still_replay_hour_for_hour(stamp, hours): + runs = sorted(LIVE.glob(f"*_{stamp}")) if LIVE.exists() else [] + if not runs: + pytest.skip("the 0.10.0 live runs are kept locally in runs/live") + m = json.loads((runs[0] / "manifest.json").read_text(encoding="utf-8")) + assert EnvelopeConfig.from_manifest(m["envelope"]) == EnvelopeConfig() and m["supervisor"]["prompt_version"] == 8 + rep = replay_run(runs[0]) + assert rep.deterministic and rep.identical_steps == hours and rep.api_calls == 0