diff --git a/.github/workflows/codspeed.yaml b/.github/workflows/codspeed.yaml new file mode 100644 index 0000000..3e33a47 --- /dev/null +++ b/.github/workflows/codspeed.yaml @@ -0,0 +1,51 @@ +name: CodSpeed + +on: + push: + branches: [main] + pull_request: + # `workflow_dispatch` allows CodSpeed to trigger backtest + # performance analysis in order to generate initial data. + workflow_dispatch: + +permissions: + contents: read + id-token: write # for OpenID Connect authentication with CodSpeed + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + benchmarks: + name: Run benchmarks + runs-on: ubuntu-latest + env: + UV_PYTHON: "3.11" + + steps: + - name: Clone repo + uses: actions/checkout@v4 + + - name: Set up uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + + - name: Free disk space in action runner + uses: endersonmenezes/free-disk-space@v3 + with: + remove_android: true + remove_dotnet: true + remove_haskell: true + rm_cmd: "rmz" + rmz_version: "3.1.1" + + - name: Install dependencies + run: uv sync --group dev + + - name: Run benchmarks + uses: CodSpeedHQ/action@v5 + with: + mode: simulation + run: uv run pytest benchmarks/ --codspeed diff --git a/README.md b/README.md index 2fc156b..f079be0 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ PyPI versions license + CodSpeed Join Discord diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py new file mode 100644 index 0000000..e9763df --- /dev/null +++ b/benchmarks/__init__.py @@ -0,0 +1 @@ +"""CodSpeed performance benchmarks for foundationforecast.""" diff --git a/benchmarks/_models.py b/benchmarks/_models.py new file mode 100644 index 0000000..463ef5a --- /dev/null +++ b/benchmarks/_models.py @@ -0,0 +1,86 @@ +"""Lightweight forecasters used to benchmark the FoundationForecast pipeline. + +These models are deliberately trivial: the goal is to measure the cost of the +library's own data plumbing (validation, splitting, merging, quantile handling) +without downloading or running any foundation model weights. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +from utilsforecast.processing import make_future_dataframe + +from foundationforecast.core.forecaster import Forecaster, QuantileConverter + + +class ConstantModel(Forecaster): + """Return a constant point forecast, plus quantiles when requested.""" + + def __init__(self, alias: str = "Constant", value: float = 1.0): + self.alias = alias + self.value = value + + def forecast( + self, + df: pd.DataFrame, + h: int, + freq: str | None = None, + level: list[int | float] | None = None, + quantiles: list[float] | None = None, + ) -> pd.DataFrame: + freq = self._maybe_infer_freq(df, freq) + qc = QuantileConverter(level=level, quantiles=quantiles) + last_times = df.groupby("unique_id")["ds"].max() + fcst = make_future_dataframe( + uids=last_times.index.tolist(), + last_times=last_times, + h=h, + freq=freq, + ) + fcst[self.alias] = self.value + if qc.quantiles: + for q in qc.quantiles: + fcst[f"{self.alias}-q-{int(q * 100)}"] = fcst[self.alias] + q + fcst = qc.maybe_convert_quantiles_to_level(fcst, models=[self.alias]) + return fcst + + +class SeasonalNaiveModel(Forecaster): + """Repeat the last seasonal period, mimicking a real model's output shape.""" + + alias = "SeasonalNaive" + + def __init__(self, season_length: int | None = None): + self.season_length = season_length + + def forecast( + self, + df: pd.DataFrame, + h: int, + freq: str | None = None, + level: list[int | float] | None = None, + quantiles: list[float] | None = None, + ) -> pd.DataFrame: + freq = self._maybe_infer_freq(df, freq) + season_length = self._maybe_get_seasonality(freq) + qc = QuantileConverter(level=level, quantiles=quantiles) + results = [] + for uid, group in df.groupby("unique_id"): + y = group["y"].to_numpy() + effective_length = min(season_length, len(y)) + future = make_future_dataframe( + uids=[uid], + last_times=pd.Series([group["ds"].max()], index=[uid]), + h=h, + freq=freq, + ) + seasonal_values = y[-effective_length:] + future[self.alias] = seasonal_values[np.arange(h) % effective_length] + results.append(future) + fcst = pd.concat(results, ignore_index=True) + if qc.quantiles: + for q in qc.quantiles: + fcst[f"{self.alias}-q-{int(q * 100)}"] = fcst[self.alias] * (1 + q) + fcst = qc.maybe_convert_quantiles_to_level(fcst, models=[self.alias]) + return fcst diff --git a/benchmarks/conftest.py b/benchmarks/conftest.py new file mode 100644 index 0000000..54ca11f --- /dev/null +++ b/benchmarks/conftest.py @@ -0,0 +1,55 @@ +"""Shared, deterministic data fixtures for the CodSpeed benchmarks.""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest +from utilsforecast.data import generate_series + + +def make_panel( + n_series: int, + length: int, + freq: str = "D", +) -> pd.DataFrame: + """Generate a deterministic panel with `n_series` series of `length` rows. + + `generate_series` is seeded by default, so the data is stable across runs. + """ + df = generate_series( + n_series=n_series, + freq=freq, + min_length=length, + max_length=length, + ) + df["unique_id"] = df["unique_id"].astype(str) + return df.reset_index(drop=True) + + +@pytest.fixture(scope="session") +def small_panel() -> pd.DataFrame: + """20 daily series of 200 observations.""" + return make_panel(n_series=20, length=200) + + +@pytest.fixture(scope="session") +def large_panel() -> pd.DataFrame: + """200 daily series of 500 observations.""" + return make_panel(n_series=200, length=500) + + +@pytest.fixture(scope="session") +def unsorted_panel(large_panel: pd.DataFrame) -> pd.DataFrame: + """A shuffled copy of `large_panel`, forcing the sorting code paths.""" + rng = np.random.default_rng(42) + idxs = rng.permutation(len(large_panel)) + return large_panel.iloc[idxs].reset_index(drop=True) + + +@pytest.fixture(scope="session") +def string_ds_panel(large_panel: pd.DataFrame) -> pd.DataFrame: + """`large_panel` with `ds` stored as ISO-8601 strings.""" + df = large_panel.copy() + df["ds"] = df["ds"].dt.strftime("%Y-%m-%d") + return df diff --git a/benchmarks/test_bench_core_utils.py b/benchmarks/test_bench_core_utils.py new file mode 100644 index 0000000..9437c34 --- /dev/null +++ b/benchmarks/test_bench_core_utils.py @@ -0,0 +1,78 @@ +"""Benchmarks for the pure-python helpers used on every forecasting call.""" + +from __future__ import annotations + +import pandas as pd +import pytest + +from foundationforecast.core.forecaster import ( + Forecaster, + QuantileConverter, + get_seasonality, + maybe_convert_col_to_datetime, + maybe_infer_freq, +) +from foundationforecast.core.gluonts_forecaster import ( + maybe_convert_col_to_float32, +) + + +def test_validate_input(benchmark, large_panel: pd.DataFrame): + benchmark(Forecaster.validate_input, large_panel, 12) + + +def test_maybe_infer_freq(benchmark, large_panel: pd.DataFrame): + freq = benchmark(maybe_infer_freq, large_panel, None) + assert freq == "D" + + +def test_maybe_convert_col_to_datetime_from_string( + benchmark, + string_ds_panel: pd.DataFrame, +): + out = benchmark(maybe_convert_col_to_datetime, string_ds_panel, "ds") + assert pd.api.types.is_datetime64_any_dtype(out["ds"]) + + +def test_maybe_convert_col_to_float32(benchmark, large_panel: pd.DataFrame): + out = benchmark(maybe_convert_col_to_float32, large_panel, "y") + assert out["y"].dtype == "float32" + + +@pytest.mark.parametrize("freq", ["D", "h", "MS"]) +def test_get_seasonality(benchmark, freq: str): + benchmark(get_seasonality, freq) + + +def test_quantile_converter_from_level(benchmark): + qc = benchmark(QuantileConverter, [80, 90, 95], None) + assert qc.quantiles is not None + + +def test_quantile_converter_level_to_quantiles( + benchmark, + large_panel: pd.DataFrame, +): + """Levels -> quantile columns, as done when users pass `quantiles`.""" + model = "Model" + df = large_panel.rename(columns={"y": model}) + qc = QuantileConverter(quantiles=[0.1, 0.25, 0.5, 0.75, 0.9]) + for lv in [50, 80]: + df[f"{model}-lo-{lv}"] = df[model] - lv / 100 + df[f"{model}-hi-{lv}"] = df[model] + lv / 100 + out = benchmark(qc.maybe_convert_level_to_quantiles, df, [model]) + assert f"{model}-q-90" in out.columns + + +def test_quantile_converter_quantiles_to_level( + benchmark, + large_panel: pd.DataFrame, +): + """Quantile columns -> lo/hi levels, as done when users pass `level`.""" + model = "Model" + df = large_panel.rename(columns={"y": model}) + qc = QuantileConverter(level=[80, 90]) + for q in qc.quantiles or []: + df[f"{model}-q-{int(q * 100)}"] = df[model] + q + out = benchmark(qc.maybe_convert_quantiles_to_level, df, [model]) + assert f"{model}-lo-90" in out.columns diff --git a/benchmarks/test_bench_dataset.py b/benchmarks/test_bench_dataset.py new file mode 100644 index 0000000..9000419 --- /dev/null +++ b/benchmarks/test_bench_dataset.py @@ -0,0 +1,63 @@ +"""Benchmarks for the tensor-side data preparation used by every model.""" + +from __future__ import annotations + +import pandas as pd +import pytest +import torch + +from foundationforecast.core.forecaster import _DataProcessor +from foundationforecast.core.utils import TimeSeriesDataset + + +@pytest.mark.parametrize("batch_size", [16, 128]) +def test_time_series_dataset_from_df( + benchmark, + large_panel: pd.DataFrame, + batch_size: int, +): + dataset = benchmark(TimeSeriesDataset.from_df, large_panel, batch_size) + assert len(dataset) > 0 + + +def test_time_series_dataset_iteration(benchmark, large_panel: pd.DataFrame): + dataset = TimeSeriesDataset.from_df(large_panel, batch_size=16) + + def consume() -> int: + return sum(len(batch) for batch in dataset) + + assert benchmark(consume) == len(large_panel["unique_id"].unique()) + + +def test_time_series_dataset_make_future_dataframe( + benchmark, + large_panel: pd.DataFrame, +): + dataset = TimeSeriesDataset.from_df(large_panel, batch_size=32) + future = benchmark(dataset.make_future_dataframe, 24, "D") + assert len(future) == 24 * len(large_panel["unique_id"].unique()) + + +def _variable_length_context( + n_series: int = 128, + max_len: int = 512, +) -> list[torch.Tensor]: + generator = torch.Generator().manual_seed(0) + return [ + torch.rand(max_len - (i % 64), generator=generator, dtype=torch.float32) + for i in range(n_series) + ] + + +def test_data_processor_left_pad_and_stack(benchmark): + processor = _DataProcessor(dtype=torch.float32, device=torch.device("cpu")) + context = _variable_length_context() + out = benchmark(processor._prepare_and_validate_context, context) + assert out.shape[0] == len(context) + + +def test_data_processor_impute_missing(benchmark): + processor = _DataProcessor(dtype=torch.float32, device=torch.device("cpu")) + batch = processor._prepare_and_validate_context(_variable_length_context()) + out = benchmark(processor._maybe_impute_missing, batch) + assert not torch.isnan(out).any() diff --git a/benchmarks/test_bench_pipelines.py b/benchmarks/test_bench_pipelines.py new file mode 100644 index 0000000..bea3408 --- /dev/null +++ b/benchmarks/test_bench_pipelines.py @@ -0,0 +1,58 @@ +"""End-to-end benchmarks of the forecast / cross-validation / anomaly APIs. + +The models used here are trivial by design (see `_models.py`): what is measured +is the library's own orchestration cost - validation, frequency inference, +backtest splitting, merging and residual statistics. +""" + +from __future__ import annotations + +import pandas as pd +import pytest + +from ._models import ConstantModel, SeasonalNaiveModel +from foundationforecast import FoundationForecast + + +def test_forecast_single_model(benchmark, large_panel: pd.DataFrame): + ff = FoundationForecast(models=[SeasonalNaiveModel()]) + fcst = benchmark(ff.forecast, large_panel, 12, "D") + assert len(fcst) == 12 * large_panel["unique_id"].nunique() + + +def test_forecast_with_levels(benchmark, large_panel: pd.DataFrame): + ff = FoundationForecast(models=[SeasonalNaiveModel()]) + fcst = benchmark(ff.forecast, large_panel, 12, "D", [80, 90]) + assert "SeasonalNaive-lo-90" in fcst.columns + + +def test_forecast_multi_model_merge(benchmark, large_panel: pd.DataFrame): + ff = FoundationForecast( + models=[ConstantModel(alias=f"Constant{i}", value=float(i)) for i in range(4)] + ) + fcst = benchmark(ff.forecast, large_panel, 12, "D") + assert "Constant3" in fcst.columns + + +def test_forecast_infer_freq_and_sort(benchmark, unsorted_panel: pd.DataFrame): + """Forecast on shuffled input, exercising freq inference and sorting.""" + ff = FoundationForecast(models=[ConstantModel()]) + fcst = benchmark(ff.forecast, unsorted_panel, 12) + assert len(fcst) == 12 * unsorted_panel["unique_id"].nunique() + + +@pytest.mark.parametrize("n_windows", [1, 4]) +def test_cross_validation( + benchmark, + small_panel: pd.DataFrame, + n_windows: int, +): + ff = FoundationForecast(models=[SeasonalNaiveModel()]) + cv_df = benchmark(ff.cross_validation, small_panel, 12, "D", n_windows) + assert len(cv_df) == 12 * n_windows * small_panel["unique_id"].nunique() + + +def test_detect_anomalies(benchmark, small_panel: pd.DataFrame): + ff = FoundationForecast(models=[SeasonalNaiveModel()]) + anomalies = benchmark(ff.detect_anomalies, small_panel, 12, "D", 3) + assert "SeasonalNaive-anomaly" in anomalies.columns diff --git a/pyproject.toml b/pyproject.toml index c9337e3..3ed0b1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,7 @@ requires = ["hatchling"] dev = [ "mktestdocs>=0.2.5", "pre-commit", + "pytest-codspeed>=4.0", "pytest-cov>=6.0", "pytest-mock>=3.15.1", "pytest-rerunfailures>=15.1", diff --git a/uv.lock b/uv.lock index 266def1..2c16fe6 100644 --- a/uv.lock +++ b/uv.lock @@ -7,7 +7,7 @@ resolution-markers = [ "python_full_version == '3.14.*'", "python_full_version >= '3.15'", ] -revision = 2 +revision = 3 version = 1 [[package]] @@ -497,7 +497,7 @@ wheels = [ [[package]] dependencies = [ - {marker = "implementation_name != 'PyPy'", name = "pycparser"}, + {name = "pycparser"}, ] name = "cffi" sdist = {hash = "sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be", size = 530807, upload-time = "2026-08-03T21:21:18.939Z", url = "https://files.pythonhosted.org/packages/9e/ef/008a1939e372c06329a3fce4279c02f328488f3526744906eeec3da7ad5f/cffi-2.1.1.tar.gz"} @@ -733,7 +733,7 @@ wheels = [ [[package]] dependencies = [ - {marker = "python_full_version < '3.11'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"}, + {name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"}, ] name = "contourpy" resolution-markers = [ @@ -803,7 +803,7 @@ wheels = [ [[package]] dependencies = [ - {marker = "python_full_version >= '3.11' and python_full_version < '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"}, + {marker = "python_full_version < '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"}, {marker = "python_full_version >= '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "2.5.2"}, ] name = "contourpy" @@ -1026,7 +1026,7 @@ toml = [ [[package]] dependencies = [ - {marker = "python_full_version < '3.15'", name = "cuda-pathfinder"}, + {name = "cuda-pathfinder"}, ] name = "cuda-bindings" source = {registry = "https://pypi.org/simple"} @@ -1064,43 +1064,43 @@ wheels = [ [package.optional-dependencies] cublas = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cublas"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-nvrtc"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cublas"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-nvrtc"}, ] cudart = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-runtime"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-runtime"}, ] cufft = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cufft"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-nvjitlink"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cufft"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-nvjitlink"}, ] cufile = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')", name = "nvidia-cufile"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cufile"}, ] cupti = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-cupti"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-cupti"}, ] curand = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-curand"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-curand"}, ] cusolver = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cublas"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cusolver"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cusparse"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-nvjitlink"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cublas"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cusolver"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cusparse"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-nvjitlink"}, ] cusparse = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cusparse"}, - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-nvjitlink"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cusparse"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-nvjitlink"}, ] nvjitlink = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-nvjitlink"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-nvjitlink"}, ] nvrtc = [ - {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-nvrtc"}, + {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-nvrtc"}, ] nvtx = [ - {marker = "(platform_machine == 'aarch64' and 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