ThermoShift

Synthetic building-cooling trajectories with paired counterfactual outcomes for machine learning.

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ThermoShift generates hourly cooling decisions for temperature prediction, action-effect estimation, one-step policy evaluation, and sensor-state modeling. Each decision includes observed context, a sampled action, its logging probability, and the resulting temperature, electricity use, cost, emissions, and comfort penalty. A paired oracle table supplies latent state and potential outcomes for all three cooling actions.

The Python package and CLI write sharded Parquet releases with deterministic building splits, resumable generation, checksums, validation reports. Run it locally or as a batch job; multi-machine generation uses a shared filesystem.

Capabilities and scope

Capability	Implementation
Paired supervision	One logged row and one oracle row per decision, joined by row_id
Ordered trajectories	One trajectory per synthetic building; exact requested decision count
Distribution shifts	Held-out buildings plus heatwave and sensor-degradation test splits
Reproducible simulation	Indexed random streams; numeric records independent of worker and batch scheduling in the same source/runtime
Bounded generation	Vectorized building batches, process workers, and bounded task scheduling
Recovery	Per-shard state, atomic file replacement, SHA-256 verification, regeneration of incomplete or corrupt shards
Release validation	File inventory, schemas, record consistency, physical equations, and optional exact replay
Consumption and sharing	Paired PyArrow batches, Hugging Face streaming, verified publication to a pinned commit
Reference experiments	Temperature regression baseline and propensity-based one-step policy evaluation

The simulator is an idealized, single-zone research model. Its validation checks internal consistency with the specified model; it does not establish accuracy for real buildings or qualify a controller for deployment. Counterfactual labels cover one hour from a logged state. Evaluating a replacement policy over an entire trajectory requires simulating the states that policy would produce.

Installation

Browse the documentation guides, or see development and documentation to build the Sphinx site locally.

Use Python 3.11 or later. Core dependencies are NumPy, PyArrow, filelock, portalocker, and PyYAML; version ranges are defined in pyproject.toml.

From PyPI:

pip install thermoshift

From a source checkout:

git clone https://github.com/neuralsorcerer/thermoshift.git
cd thermoshift
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[hub,analysis]"
python -m thermoshift --version
python -m thermoshift --help

On Windows PowerShell, activate with .venv\Scripts\Activate.ps1. Run the examples below from the repository root with that environment active. Both thermoshift and python -m thermoshift invoke the same CLI.

For a generator-only installation, use python -m pip install .. For a deployed batch environment, install a built wheel and retain the exact source and dependency versions used to initialize each run. Editable installs are convenient for local development, but package edits change the generation fingerprint.

Extra	Install from the repository	Enables
hub	python -m pip install ".[hub]"	Hugging Face publishing and Datasets loading
analysis	python -m pip install ".[analysis]"	scikit-learn temperature baseline and pandas analysis
notebook	python -m pip install ".[notebook]"	Notebook format, kernel, and display dependencies
dev	python -m pip install -e ".[dev]"	Tests, linting, formatting, scientific checks, and distribution builds

Combine extras as needed. constraints.txt records development dependency versions; apply it with -c constraints.txt when reproducing that setup. It is a constraints file, not a complete lock of every transitive dependency.

Quickstart

Create and validate a small release before scaling up:

python -m thermoshift init output/demo --rows 10000 --seed 42
python -m thermoshift plan output/demo
python -m thermoshift generate output/demo
python -m thermoshift validate output/demo

This creates exactly 10,000 decisions across 60 buildings, using the default 168-hour episode length. The final building has 88 stored steps. Each decision appears once in each table, so the release contains 10,000 logged rows and 10,000 oracle rows. generate automatically finalizes a single-rank run; a successful full validation returns JSON containing "status": "passed", "scope": "full", and "rows": 10000.

The equivalent Python workflow is:

from thermoshift import Config, generate, initialize, validate

initialize("output/python-demo", Config(rows=10_000, seed=42))
generation = generate("output/python-demo", batch_buildings=256)
report = validate("output/python-demo")
assert report["status"] == "passed"
print(generation["written"], report["rows"])

Repeat generate to resume a run, then validate again. Use a new directory when changing the configuration, source, or recorded runtime. See production operations for recovery and release handling.

Dataset contract

Tables and identifiers

Configuration	Contents	Intended use
logged	Decision-time observations, action, action probabilities, factual targets, episode boundary	Observed-feature training and propensity-based estimation
oracle	True temperature, physical parameters, sensor labels, six potential outcomes per action, best one-step action	Evaluation or explicitly declared privileged supervision

The common identifiers are row_id, building_id, and step:

row_id = building_id * episode_steps + step

row_id is a signed 64-bit identifier unique within a release. It is not a global identifier across experiments. Records are ordered by building and step within partitions; the paired reader visits shards in numeric order within each split. episode_end marks the final stored step, including a shortened final trajectory.

Field group	Examples	Meaning
Observations	outdoor_temp_c, occupancy_count, price_per_kwh, grid_available	Context available at the decision time
Sensor state	obs_temp_c, obs_temp_last_c, sensor_age_steps	Current nullable reading, latest available reading, and reading age
Action	action	0: off; 1: 50% rated electrical input; 2: 100% rated electrical input
Logging probabilities	p_action_0, p_action_1, p_action_2, propensity	Actual sampling probabilities; propensity is the selected action's probability
Factual outcomes	y_next_temp_c, y_energy_kwh, y_cost, y_carbon_kg, y_discomfort_c, y_reward	Observed transition under the sampled action
Oracle outcomes	cf_next_temp_c_0, cf_reward_0, corresponding fields for actions 1 and 2	Potential outcomes sharing the current state and hourly disturbances

Physical calculations use float64; measured features and outcomes are stored as float32, while action probabilities remain float64. Sensor dropout is an Arrow null in obs_temp_c. obs_temp_last_c retains the latest reading and falls back to the setpoint if the episode begins with dropout.

docs/schema.md lists every column, type, unit, role, and nullability. Generated schema.json and feature_roles.json provide machine-readable contracts.

Splits

A hash of building ID and seed assigns each building to exactly one split. The percentages below describe hash-bucket allocation; realized building and row counts vary and are recorded in manifest.json. Very small releases may have empty splits, which are omitted from the generated loading configuration.

Split	Allocation	Conditions
train	70%	Standard generating conditions
validation	10%	Standard conditions, disjoint buildings
test	10%	Standard conditions, disjoint buildings
test_heatwave	5%	Outdoor offset +8 °C, COP multiplied by 0.85, increased outage-entry probability
test_sensor	5%	Stronger sensor drift, more frequent and persistent dropout

Preserve these building splits for held-out evaluation. Preserve complete ordered trajectories for sequence models; random row splitting leaks information between hours from the same building.

Release files

Path	Purpose
data/logged/<split>/<bucket>/part-*.parquet	Observed decisions and factual outcomes
data/oracle/<split>/<bucket>/part-*.parquet	Matching latent state and counterfactual outcomes
run_config.json	Immutable configuration, config/source fingerprints, recorded runtime
schema.json, feature_roles.json	Types, units, roles, and feature lists
manifest.json	Exact file inventory, row counts, compressed sizes, SHA-256 hashes
README.md, DATASHEET.md, LICENSE	Generated dataset card, model reference, and license
_SUCCESS.json	Completion marker binding the manifest and release metadata
validation.json	Latest saved validation status, scope, and split profiles
_state/shard-*.json	Local committed-shard records used for recovery
_PUBLICATION.json, _state/hub_binding.json	Publication intent and local target binding, created when publishing

Buckets group 1,000 shard IDs. Each shard writes one file per nonempty split per configuration, with at most ten Parquet files per shard. Files use Zstandard compression, level 3 by default.

Load and use the data

Stream with Hugging Face Datasets

Requires the hub extra:

from datasets import load_dataset

records = load_dataset(
    "output/demo",
    name="logged",
    split="train",
    streaming=True,
    columns=["row_id", "building_id", "step", "obs_temp_last_c", "action", "y_next_temp_c"],
)

for record in records.take(3):
    print(record)

For a published release, replace the directory with OWNER/DATASET and pass revision="COMMIT_SHA_FROM_PUBLISH_OUTPUT". Load name="oracle" to access oracle labels. Join by row_id when streams have been shuffled or partitioned separately. External loaders read the Parquet data; run ThermoShift validation separately when you need its release-integrity and scientific checks.

Read verified pairs with PyArrow

The core package can stream both tables without the Hub extra:

from contextlib import closing

from thermoshift import iter_pairs

with closing(iter_pairs("output/demo", split="test", batch_size=1024)) as batches:
    for file_info, logged, oracle in batches:
        assert logged["row_id"].equals(oracle["row_id"])
        print(file_info["split"], logged.num_rows)
        break

iter_pairs verifies release metadata and file checksums before yielding its first batch. It holds an exclusive dataset lease until exhausted or closed; the context manager releases it even when stopping early. It verifies file integrity but does not run the full record-level validation scan.

Select features and run reference experiments

Use the declared feature lists to keep targets and privileged state out of model inputs:

from thermoshift.schema import feature_roles

roles = feature_roles()
policy_columns = roles["policy_features"]
transition_columns = roles["transition_features"]  # Also includes the action.

python examples/train_baseline.py --data output/demo
python examples/evaluate_policy.py --data output/demo --split test
python examples/stream_hf.py output/demo --config logged --split train --limit 3

The temperature baseline requires analysis. It fits a gradient-boosted regressor on a bounded training prefix and reports MAE/RMSE against persistence on each nonempty evaluation split. Use --max-train, --max-eval, and --report to control its limits and save results.

The policy example uses the core dependencies. It evaluates a fixed observed-gap threshold policy with inverse propensity scoring (IPS), self-normalized IPS (SNIPS), oracle policy value, and one-step regret. Uncertainty is clustered by building. It reports missing intervals when overlap or building counts are insufficient; row-weight effective sample size is an overlap diagnostic. Its --report option saves the JSON result.

The complete Python and CLI contracts, including Config, in-memory simulation, validation reports, verified paired readers, schema helpers, publication plans, and notebook execution, are in the Python API reference.

Bundled sample and notebook

sample_dataset/ contains 50,003 decisions from 298 buildings and can be loaded by substituting sample_dataset in the loading examples. It is included in the repository, but excluded from the wheel and source distribution.

The sample records a different generator fingerprint and was generated with Python 3.12.14 and ThermoShift 0.1.0. Its checked-in validation.json records a passing full validation and deterministic replay for that recorded source and runtime. Replay validation is intentionally provenance-sensitive; generate a fresh release when validating the current source or runtime.

The walkthrough explores the bundled sample's tables, splits, feature roles, and paired outcomes. It verifies the sample's current file integrity and labels its saved scientific-validation profiles as historical. For a release generated by the current source and runtime, it performs a fresh full validation instead:

python -m pip install -e ".[hub,analysis,notebook]"
python scripts/run_notebook.py

Open notebooks/quickstart.ipynb to inspect it. The runner executes trusted plain-Python cells in process and saves outputs into the notebook; it is not a sandbox or a general Jupyter server.

Simulation and scientific interpretation

Each building follows an hourly single-zone resistance–capacitance thermal model. Let T and T_out be indoor and outdoor temperature, G conductance in kW/°C, C heat capacity in kWh/°C, Q total internal/solar/residual heat in kW, P rated electrical input in kW, COP cooling efficiency, and g grid availability. The action selects an input fraction a in {0, 0.5, 1}. For constant hourly inputs:

alpha = 1 - exp(-G * 1 hour / C)
T_next(a) = T + alpha * (T_out - T + (Q - COP * P * a * g) / G)
energy(a) = g * (background_load + P * a) * 1 hour
cost(a) = price * energy(a)
carbon(a) = carbon_intensity * energy(a)
discomfort(a) = max(abs(T_next(a) - setpoint) - 1, 0)
reward(a) = -(cost(a) + carbon_weight * carbon(a)
             + comfort_weight * occupancy_weight * discomfort(a)^2)

The occupancy weight is 1 when occupied and 0.1 otherwise. A grid outage sets served electricity and cooling to zero. The sampled action's temperature advances the trajectory; all counterfactual branches share that hour's initial state and disturbances. oracle_action maximizes serialized one-step reward, breaking ties with the smallest action index.

The logging policy mixes a temperature/price-dependent softmax with uniform exploration. Each action has probability at least exploration / 3 (0.05 at the default setting). With --hidden-confounding, latent temperature also affects assignment; use the supplied true propensities, since probabilities re-estimated from observed features alone generally do not reproduce that assignment process.

These semantics matter when interpreting results:

• Policies use decision-time policy_features; outcome and oracle fields are targets or evaluation information unless privileged training is explicitly intended.
• Policy estimates describe one-step reward on the logging-policy state distribution, not the long-run value of a replacement controller.
• episode_end denotes stored trajectory truncation. Define termination and bootstrapping behavior explicitly when constructing a sequential-control task.
• Synthetic weather, occupancy, tariffs, emissions, and comfort penalties are illustrative assumptions. Internal equation checks do not measure real-world fidelity.

See the model and parameter reference for the complete distributions, sensing process, logging equation, and units.

CLI and configuration

Every subcommand accepts --help for its exact interface:

Command	Purpose	Key options
init OUTPUT	Create an immutable plan in an empty directory, or reopen a matching plan	Generation parameters below
plan OUTPUT	Report decisions, buildings, shards, maximum file count, and batch row bound	--batch-buildings
generate OUTPUT	Generate or resume assigned shards; finalize single-rank runs	--workers, --batch-buildings, --rank, --world-size, --quiet
finalize OUTPUT	Verify all shards and commit release metadata after multi-rank generation	—
validate OUTPUT	Check a finalized release and save a report	--batch-size, --metadata-only, --replay, --no-report
publish OUTPUT	Validate, upload, verify, and mark a Hub release complete	--repo-id, --public, --revision, --dry-run

Parameters fixed at initialization

Option	Default	Constraint / meaning
--rows	1000000	Exact decisions; positive signed 64-bit integer
--seed	42	Integer in [0, 2^32)
--episode-steps	168	Hours per building, from 2 through 8,760
--shard-buildings	16384	Buildings per shard, from 1 through 1,000,000
--exploration	0.15	Uniform policy-mixture weight in (0, 1]
--hidden-confounding	Off	Include latent temperature in action assignment
--comfort-weight	0.30	Squared endpoint discomfort weight, in [0, 1000000)
--carbon-weight	0.05	Operational emissions weight, in [0, 1000000)

The Python Config additionally exposes compression="zstd", compression_level=3 (supported levels 1–19), and schema_version="2.0". The CLI does not expose compression flags. Do not edit run_config.json in place.

Scheduling and automation

Generation defaults to --workers 1 --batch-buildings 512 --rank 0 --world-size 1. Worker and batch counts must be positive; ranks satisfy 0 <= rank < world_size. Validation defaults to batches of 65,536 rows.

Successful commands write one final JSON result to stdout. Generation writes per-shard JSON progress to stderr; --quiet suppresses that progress. For example, after initializing output/demo:

python -m thermoshift generate output/demo \
  > output/demo-generation.json 2> output/demo-generation.log

The CLI returns 0 on success, 2 for argument errors and handled ValueError/OSError failures, and 130 for a keyboard interruption. Automation should treat every nonzero exit as failure; unexpected exceptions can have other nonzero statuses. See docs/api.md for return values, callbacks, in-memory simulation, and Python multiprocessing examples.

Production operations

Size a run before scaling

python benchmarks/generation.py \
  --output output/pilot \
  --rows 2000000 \
  --workers 2 \
  --batch-buildings 512 \
  --shard-buildings 4096 \
  --report output/pilot-benchmark.json

The benchmark requires a fresh directory and measures generation, full validation, compressed bytes per decision across both tables, and split profiles. Its memory metric is parent-process peak RSS; measure total job memory separately when using multiple workers. See benchmarks/README.md.

Batch size controls simulation memory. Each worker simulates up to min(batch_buildings, shard_buildings) * episode_steps transitions at once, with additional Arrow, filtering, and compression allocations. Shard size controls file count, parallel work, and how much must be rebuilt after a failure. Workers are capped by assigned shard count: increasing workers cannot parallelize a one-shard run. File inventories and finalization metadata grow with shard count.

For example, a billion-decision plan with the default episode and shard sizes has 5,952,381 buildings, 364 shards, and at most 3,640 Parquet files:

python -m thermoshift init output/large --rows 1000000000 --seed 42
python -m thermoshift plan output/large --batch-buildings 256
python -m thermoshift generate output/large --workers 8 --batch-buildings 256
python -m thermoshift validate output/large

Choose hardware and disk capacity from measured pilot results. plan reports counts and batch bounds, not a throughput, disk-size, or RAM guarantee. Account for temporary files and checksum/full-validation I/O as well as the final payload.

Reproducibility and recovery

Initialization records a configuration fingerprint, a digest of package Python files and packaged Markdown resources, and exact Python, ThermoShift, NumPy, and PyArrow versions. Generation, finalization, and replay require that recorded source and runtime. Preserve the built artifact and environment for the life of the run.

Indexed random draws make numeric records stable across worker counts, batch sizes, and shard scheduling with the same scientific settings and runtime. Changing batch boundaries can change Parquet row groups and compressed bytes, so numeric equality does not imply identical file hashes. Increasing the requested size in a new run preserves the existing decision prefix when seed, episode length, and model settings match, except for the previously truncated episode_end marker.

To recover an interrupted local release:

python -m thermoshift generate output/large --workers 8 --batch-buildings 256
python -m thermoshift validate output/large

Completed shards are checksum-verified; incomplete or corrupt shards are rebuilt. Parquet files are closed, flushed, atomically renamed, and hashed before shard state is committed. Starting generation clears the previous completion marker and saved validation report; finish generation and revalidate before consuming the release. Keep _state/ with the local output to retain its committed-shard recovery records.

Multiple machines or job ranks

Use a shared filesystem supporting advisory locks and atomic same-directory renames, with matching package source and recorded runtime on every rank. Initialize once; shard i is assigned to rank i % world_size:

python -m thermoshift init /shared/thermoshift --rows 1000000000

# Run these as separate jobs against the same initialized directory.
python -m thermoshift generate /shared/thermoshift --world-size 2 --rank 0 --workers 8
python -m thermoshift generate /shared/thermoshift --world-size 2 --rank 1 --workers 8

# Only after every rank has finished successfully:
python -m thermoshift finalize /shared/thermoshift
python -m thermoshift validate /shared/thermoshift

Generation ranks hold shared lifecycle leases and per-shard locks. Finalization, validation, publication, and paired readers require exclusive leases. Conflicting operations fail immediately with a busy error. Close readers before starting another exclusive operation, and ensure all writers follow the locking protocol. Generation takes filesystem paths; generate locally or on the shared filesystem before uploading to remote storage.

Validation and release handoff

# Full file-integrity and scientific record checks; writes validation.json.
python -m thermoshift validate output/demo

# Hashes, inventory, schemas, and counts; no scientific record scan.
python -m thermoshift validate output/demo --metadata-only

# Full checks plus exact regeneration of every building.
python -m thermoshift validate output/demo --replay

# Run full checks without replacing the saved report.
python -m thermoshift validate output/demo --no-report

--metadata-only still reads file bytes to verify hashes. --replay requires full validation and cannot be combined with --metadata-only. Exact replay adds work proportional to regenerating the release, one building at a time.

Full validation checks IDs, split membership, trajectory and sensor continuity, logging probabilities, factual/oracle alignment, physical domains, thermal and electrical equations, reward accounting, cooling monotonicity, and oracle labels. Reports use running, passed, failed, or interrupted status and record the check scope. A killed process can leave running; rerun validation to refresh it. Each report-writing run replaces the previous report, including metadata-only runs.

For a handoff, retain _SUCCESS.json, its bound manifest and metadata, the full payload, and a passing full-scope validation report. The completion marker records finalization; it does not itself certify a full scientific validation. Treat finalized release files as immutable and validate transferred copies before use. Publication performs a fresh full local validation regardless of a saved report.

Troubleshooting

Symptom	Resolution
Missing command options or imports after updating the checkout	Confirm the active environment with python -c "import thermoshift; print(thermoshift.__file__)"; reinstall this checkout with python -m pip install -e ".[hub,analysis]". An older installed package can otherwise take precedence.
Output contains a different configuration / new output must be empty	Initialize a new empty directory with the intended parameters.
Generator code/runtime differs from initialization	Restore the recorded source and exact runtime, or start a new release directory.
dataset is busy	Finish active operations and exhaust or close paired iterators before retrying.
Shard is incomplete during finalization	Complete all ranks, then run finalize and full validation.
Parquet checksum fails	Resume generation in the original environment to rebuild the affected shard, then validate.
Release metadata checksum fails	Restore the original bound metadata; do not hand-edit a finalized card, schema, or manifest.
Observable logging policy is not exactly reproducible	Check the release source/runtime provenance, especially for the bundled sample. Generate and validate a fresh release in the active environment.
No rows for a requested split / too few training rows	Inspect manifest.json; use a larger release because small building samples can leave splits empty.
Memory pressure or poor worker utilization	Reduce batch size or workers; check plan to ensure enough shards exist for parallel work.
Publication target, visibility, or parent-commit conflict	Check the bound repository/branch and remote changes; resume the matching target or use a separate release. See the publishing guide.

Development and verification

Install all development and example dependencies, then run the repository checks:

python -m pip install -e ".[dev,hub,analysis,notebook]"
python -m pip check
python -m ruff check src tests examples scripts benchmarks
python -m ruff format --check src tests examples scripts benchmarks
python scripts/update_schema.py --check
python -m pytest -q

Tests cover independent thermal calculations, deterministic batching, int64 row boundaries, split isolation, corrupted records and files, interruption recovery, process leases, policy uncertainty, CLI behavior, notebook failures, and publication. Publication tests use an in-memory Hub service that checks installed SDK signatures and commit-parent semantics; they do not certify live account permissions or quotas.

Build and verify the distributable artifacts:

python -m build
python scripts/check_distribution.py

The distribution check expects one wheel and one source archive in dist/. It checks their contents against the checkout, installs the wheel into a temporary target, and generates, validates, and exactly replays a small dataset from that installation. The package version is defined in src/thermoshift/_version.py.

When changing column definitions, regenerate the reference with python scripts/update_schema.py. Follow CONTRIBUTING.md for scientific regression expectations and documentation maintenance. Report bugs or propose changes through the repository's issue tracker, including the command, package/runtime versions, relevant configuration, and error output.

License

ThermoShift is licensed under the MIT license.