diff --git a/.coverage b/.coverage new file mode 100644 index 0000000..a2806d1 Binary files /dev/null and b/.coverage differ diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..17b9f39 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,3 @@ +[run] +omit = + */plotting.py \ No newline at end of file diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml new file mode 100644 index 0000000..81a1384 --- /dev/null +++ b/.github/workflows/tests.yaml @@ -0,0 +1,39 @@ +name: tests + +on: + push: + pull_request: + +jobs: + pytest: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "$HOME/.cargo/bin" >> $GITHUB_PATH + + - name: Create venv + run: uv venv + + - name: Install dependencies + run: uv pip install -e .[dev] + + - name: Run tests (push) + if: github.event_name == 'push' + run: uv run pytest + + - name: Run tests + coverage gate (PR into main) + if: github.event_name == 'pull_request' && github.base_ref == 'main' + run: uv run pytest --cov=voice --cov-report=term-missing --cov-fail-under=90 diff --git a/.gitignore b/.gitignore index 8b7aeff..b11f7fc 100644 --- a/.gitignore +++ b/.gitignore @@ -52,3 +52,6 @@ htmlcov/ # Local configs .env *.local + +# Notebooks +**.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 40b62bd..d652fa0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -58,25 +58,31 @@ repos: hooks: - id: isort - # Type checking + # Type checking (keep for src; exclude tests) - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.13.0 hooks: - id: mypy args: ["--config-file", "pyproject.toml"] + exclude: ^tests/ - # Docstring correctness (sphinx convention) + # Docstring correctness (sphinx convention) (keep for src; exclude tests) - repo: https://github.com/jsh9/pydoclint rev: 0.7.6 hooks: - id: pydoclint args: ["--config=pyproject.toml"] + exclude: ^tests/ - # Cognitive complexity limit via flake8-cognitive-complexity + # Cognitive complexity limit via flake8-cognitive-complexity (keep for src; exclude tests) - repo: https://github.com/pycqa/flake8 rev: 7.3.0 hooks: - id: flake8 additional_dependencies: - flake8-cognitive-complexity - args: ["--max-cognitive-complexity=10"] + args: + - --max-line-length=79 + - --extend-ignore=E203 + - --max-cognitive-complexity=10 + exclude: ^tests/ diff --git a/cspell.json b/cspell.json index acf895b..b5087af 100644 --- a/cspell.json +++ b/cspell.json @@ -28,6 +28,7 @@ "cspell/**", "LICENSE", ".secrets.baseline", - "*.egg-info/" + "*.egg-info/", + "**/*.ipynb" ] } diff --git a/cspell/library-words.txt b/cspell/library-words.txt index e69de29..9b8fe90 100644 --- a/cspell/library-words.txt +++ b/cspell/library-words.txt @@ -0,0 +1,10 @@ +huggingface +resp +passthrough +figsize +whitegrid +kdeplot +xlabel +ylabel +frameon +allclose diff --git a/cspell/project-words.txt b/cspell/project-words.txt index e69de29..a89cdc5 100644 --- a/cspell/project-words.txt +++ b/cspell/project-words.txt @@ -0,0 +1,13 @@ +hapax +dis +tri +legomenon +legomena +MATTR +TTR +ngram +prov +wasserstein +bonf +bonferroni +aeiou diff --git a/pyproject.toml b/pyproject.toml index 05fd12f..a11d076 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,14 @@ version = "0.1.0" description = "Fine-tuning for stylistic fidelity" readme = "README.md" requires-python = ">=3.12" -dependencies = [] +dependencies = [ + "datasets>=4.5.0", + "dotenv>=0.9.9", + "huggingface-hub>=1.4.1", + "matplotlib>=3.10.8", + "scipy>=1.17.0", + "seaborn>=0.13.2", +] [build-system] requires = ["setuptools>=68", "wheel"] @@ -20,15 +27,19 @@ dev = [ "flake8-cognitive-complexity>=0.1.0", "isort>=7.0.0", "mypy>=1.19.1", + "notebook>=7.5.3", "pre-commit>=4.5.1", "pydoclint>=0.8.3", + "pytest>=9.0.2", + "pytest-cov>=7.0.0", "ruff>=0.14.14", ] [tool.ruff] -line-length = 88 +line-length = 79 target-version = "py312" force-exclude = true +extend-ignore = ["D107"] [tool.ruff.lint] select = [ @@ -49,9 +60,7 @@ select = [ "C901", ] -ignore = [ - "ANN101", "ANN102", -] +ignore = [] [tool.ruff.lint.mccabe] max-complexity = 12 @@ -59,15 +68,45 @@ max-complexity = 12 [tool.ruff.lint.pydocstyle] convention = "pep257" +[tool.ruff.lint.per-file-ignores] +"tests/**/*.py" = [ + "D", + "ANN", + "ARG001", + "ARG005", + "B007", +] + [tool.mypy] python_version = "3.12" strict = true warn_unused_configs = true no_implicit_optional = true +[[tool.mypy.overrides]] +module = ["tests.*"] +ignore_errors = true + +[[tool.mypy.overrides]] +module = [ + "datasets", + "datasets.*", + "huggingface_hub", + "huggingface_hub.*", + "numpy", + "numpy.*", + "scipy.stats", + "scipy.stats.*", + "seaborn", + "seaborn.*", + "matplotlib", + "matplotlib.*", +] +ignore_missing_imports = true + [tool.isort] profile = "black" -line_length = 88 +line_length = 79 [tool.pydoclint] style = "sphinx" diff --git a/src/voice/__init__.py b/src/voice/__init__.py index b820829..1abc425 100644 --- a/src/voice/__init__.py +++ b/src/voice/__init__.py @@ -4,3 +4,13 @@ This package provides tools for fine-tuning and evaluating LLMs for stylistic fidelity. """ + +from voice.datasets import DatasetSpec, get_dataset +from voice.stylometry import get_metrics, make_comparison + +__all__: list[str] = [ + "DatasetSpec", + "get_metrics", + "get_dataset", + "make_comparison", +] diff --git a/src/voice/datasets/__init__.py b/src/voice/datasets/__init__.py new file mode 100644 index 0000000..696e12e --- /dev/null +++ b/src/voice/datasets/__init__.py @@ -0,0 +1,15 @@ +""" +Datasets package for VOICE. + +This package contains user-facing abstractions for dataset management, +in particular we expose: + +- `DatasetSpec` - Lightweight, declarative specification of a dataset. +- `VoiceDataset` - Split-aware wrapper around a Hugging Face dataset. +- `get_dataset` - Entrypoint for materialising a dataset from a specification. +""" + +from voice.datasets.dataset import DatasetSpec, VoiceDataset +from voice.datasets.get_dataset import get_dataset + +__all__: list[str] = ["DatasetSpec", "VoiceDataset", "get_dataset"] diff --git a/src/voice/datasets/_schema.py b/src/voice/datasets/_schema.py new file mode 100644 index 0000000..6063a36 --- /dev/null +++ b/src/voice/datasets/_schema.py @@ -0,0 +1,67 @@ +""" +Dataset schema primitives. + +This module defines lightweight schema-level abstractions shared across +the dataset loading pipeline. + +This module is private by convention and not part of the public API. +""" + +from __future__ import annotations + +from enum import Enum + + +class Split(str, Enum): + """ + Enumeration of supported dataset splits. + + .. attribute :: TRAIN + + Name of the training split + + .. attribute :: VALIDATION + + Name of the validation split + + .. attribute :: TEST + + Name of the test split + """ + + TRAIN = "train" + VALIDATION = "validation" + TEST = "test" + + def __repr__(self) -> str: + """ + Return the split as a string. + + :return: Readable string of the split + """ + return f"'{self.value}'" + + @classmethod + def parse(cls, value: Split | str) -> Split: + """ + Parse a split value from a string or Split instance. + + :param value: Split name or Split enum value + :return: Parsed Split enum value + :raises TypeError: If the value is not a Split or string + :raises ValueError: If the value does not correspond to a valid split + """ + if isinstance(value, cls): + return value + if not isinstance(value, str): + raise TypeError( + f"`split` must be a Split or str, got {type(value).__name__}" + ) + v = value.strip().lower() + try: + return cls(v) + except ValueError as e: + allowed = ", ".join(s.value for s in cls) + raise ValueError( + f"Invalid split {value!r}. Allowed: {allowed}" + ) from e diff --git a/src/voice/datasets/_specs.py b/src/voice/datasets/_specs.py new file mode 100644 index 0000000..ad5cc83 --- /dev/null +++ b/src/voice/datasets/_specs.py @@ -0,0 +1,16 @@ +""" +Pre-defined dataset specifications for testing. + +All specifications are of type `voice.datasets.DatasetSpec`. + +This module is private by convention and not part of the public API. +""" + +from voice.datasets import DatasetSpec +from voice.datasets._schema import Split + +BUSH_LATEST = DatasetSpec( + repo_id="AccelerateScience/bush-dataset", + revision="main", + splits=(Split.TRAIN, Split.VALIDATION, Split.TEST), +) diff --git a/src/voice/datasets/dataset.py b/src/voice/datasets/dataset.py new file mode 100644 index 0000000..9e7d821 --- /dev/null +++ b/src/voice/datasets/dataset.py @@ -0,0 +1,463 @@ +""" +Core dataset abstractions for the `voice.datasets` package. + +This module defines: + +- `DatasetSpec` - A lightweight declarative specification of a dataset + hosted on Hugging Face. +- `_PinnedDatasetSpec` - An internal revision-pinned specification + resolved to a specific git commit SHA. +- `Example` - A representation of a single question-answer example + with provenance metadata. +- `VoiceDataset` — A split-aware wrapper providing structured access + to examples across dataset splits. +""" + +from __future__ import annotations + +import re +from collections.abc import Callable, Iterator, Mapping, Sequence +from dataclasses import dataclass +from typing import Any, NamedTuple, overload + +from datasets import Dataset + +from voice.datasets._schema import Split + +# ----------------------------------------------------------------------------- +# Dataset specification and revision pinning +# ----------------------------------------------------------------------------- + + +_SHA_RE = re.compile(r"[0-9a-f]{7,40}$") + + +@dataclass(frozen=True, slots=True) +class DatasetSpec: + """ + Lightweight specification of a Hugging Face dataset. + + .. attribute :: repo_id + + Hugging Face dataset repository id, i.e. namespace/name + + .. attribute :: revision + + Revision of the dataset (branch name or git commit hash) + + .. attribute :: splits + + Dataset splits to include (options: 'train','validation','test') + """ + + repo_id: str + revision: str = "main" + splits: tuple[Split, ...] = (Split.TRAIN,) + + def __post_init__(self) -> None: + """ + Perform local validation and normalization of the specification. + + :return: None + :raises ValueError: + - If `repo_id` is not of the form 'namespace/name' + - If `revision` is empty + - If `splits` is empty + """ + if ( + not self.repo_id + or self.repo_id.count("/") != 1 + or any(not part for part in self.repo_id.split("/")) + ): + raise ValueError( + f"`repo_id` must look like 'namespace/name'," + f" got {self.repo_id!r}" + ) + if not self.revision: + raise ValueError("`revision` must be non-empty") + if not self.splits: + raise ValueError("`splits` must be non-empty") + + splits = ( + (self.splits,) + if isinstance(self.splits, (str, Split)) + else self.splits + ) + norm = tuple(Split.parse(s) for s in splits) + object.__setattr__(self, "splits", norm) + + def _pin( + self, resolve_revision: Callable[[str, str], str] + ) -> _PinnedDatasetSpec: + """ + Resolve the dataset revision to a commit hash. + + :param resolve_revision: Callable resolving (repo_id, revision) + to a git commit hash + :return: A pinned dataset specification with a git commit hash revision + """ + return _PinnedDatasetSpec.from_spec(self, resolve_revision) + + +@dataclass(frozen=True, slots=True) +class _PinnedDatasetSpec: + """ + Internal pinned dataset specification. + + Instances of this class are guaranteed to reference a specific dataset + state via a commit hash. + + This class is private by convention and not part of the public API + + .. attribute :: repo_id + + Hugging Face dataset repository id, i.e. namespace/name + + .. attribute :: revision + + Revision of the dataset (branch name or git commit hash) + + .. attribute :: splits + + Dataset splits to include (options: 'train','validation','test') + """ + + repo_id: str + revision: str + splits: tuple[Split, ...] = (Split.TRAIN,) + + def __post_init__(self) -> None: + """ + Validate invariants for a pinned dataset specification. + + :return: None + :raises ValueError: If `revision` is not commit hash-like. + """ + if not _SHA_RE.fullmatch(self.revision): + raise ValueError( + f"`revision` must be a git SHA, got {self.revision!r}" + ) + + def __repr__(self) -> str: + """ + Return the pinned specification as a `DatasetSpec` representation. + + :return: DatasetSpec string representation + """ + return ( + f"DatasetSpec(" + f"repo_id={self.repo_id!r}, " + f"revision={self.revision!r}, " + f"splits={self.splits!r}" + f")" + ) + + @classmethod + def from_spec( + cls, spec: DatasetSpec, resolve_revision: Callable[[str, str], str] + ) -> _PinnedDatasetSpec: + """ + Create a pinned dataset specification from a user-facing spec. + + If the input spec already contains a commit hash, it is reused + directly; otherwise the revision is resolved via the provided + resolver. + + :param spec: Dataset specification to pin + :param resolve_revision: Callable resolving (repo_id, revision) + to a git commit hash + :return: A pinned dataset specification + """ + rev = spec.revision + if not _SHA_RE.fullmatch(rev): + rev = resolve_revision(spec.repo_id, rev) + return cls(repo_id=spec.repo_id, revision=rev, splits=spec.splits) + + +# ----------------------------------------------------------------------------- +# Examples and sequences +# ----------------------------------------------------------------------------- + + +class Provenance(NamedTuple): + """ + Provenance information for an Example. + + .. attribute :: spec + Revision pinned dataset specification of the source dataset. + + .. attribute :: split + Dataset split of origin (options: 'train','validation','test'). + """ + + spec: _PinnedDatasetSpec + split: Split + + +@dataclass(frozen=True, slots=True) +class Example: + """ + Single example from a QA dataset. + + .. attribute :: system + System prompt in example. + + .. attribute :: question + Question prompt in example. + + .. attribute :: answer + Answer prompt in example. + + .. attribute :: spec + Revision pinned dataset specification of source. + + .. attribute :: split + Dataset split of origin (options: 'train','validation','test'). + """ + + system: str + question: str + answer: str + spec: _PinnedDatasetSpec + split: Split + + def __getitem__(self, key: str) -> str: + """ + Allow key access of example object. + + :param key: key to access e.g. 'system' + :return: value of key (string) + :raises KeyError: If key is not in example. + """ + if key == "system": + return self.system + if key == "question": + return self.question + if key == "answer": + return self.answer + raise KeyError(key) + + @property + def provenance(self) -> Provenance: + """ + Convenience accessor for the example's origin metadata. + + :return: (spec, split) provenance tuple + """ + return Provenance(spec=self.spec, split=self.split) + + +class _ExampleSeq(Sequence[Example]): + """ + Lightweight adapter that presents one split as a `Sequence[Example]`. + + Responsible for converting Hugging Face rows into `Example` objects, + while attaching provenance (`spec`, `split`) to each example. + + This class is private by convention and not part of the public API. + + :param hf_dataset: Hugging Face datasets.Dataset + :param spec: Pinned dataset specification + :param split: Split of origin for all yielded examples + :param system_col: Column name of the system prompt column + :param question_col: Column name of the question prompt column + :param answer_col: Column name of the answer prompt column + """ + + def __init__( + self, + hf_dataset: Dataset, + *, + spec: _PinnedDatasetSpec, + split: Split, + system_col: str, + question_col: str, + answer_col: str, + ) -> None: + self._ds = hf_dataset + self._spec = spec + self._split = split + self._system_col = system_col + self._question_col = question_col + self._answer_col = answer_col + + def __len__(self) -> int: + """ + Return the number of examples in the split. + + :return: Number of examples in the split + """ + return len(self._ds) + + @overload + def __getitem__(self, idx: int) -> Example: ... + + @overload + def __getitem__(self, idx: slice) -> _ExampleSeq: ... + + def __getitem__(self, idx: int | slice) -> Example | _ExampleSeq: + """ + Get a single example (row) from the split. + + Get a slice (sequence of examples) from the split. + + :param idx: Index of the row + :return: Example object representing the indexed row + """ + if isinstance(idx, slice): + # Prefer HF native selection if available + if hasattr(self._ds, "select"): + start, stop, step = idx.indices(len(self._ds)) + indices = list(range(start, stop, step)) + subset = self._ds.select(indices) + else: + # Fallback + subset = self._ds[idx] + + return _ExampleSeq( + hf_dataset=subset, + spec=self._spec, + split=self._split, + system_col=self._system_col, + question_col=self._question_col, + answer_col=self._answer_col, + ) + + row: dict[str, Any] = self._ds[int(idx)] + return Example( + system=row[self._system_col], + question=row[self._question_col], + answer=row[self._answer_col], + spec=self._spec, + split=self._split, + ) + + def __iter__(self) -> Iterator[Example]: + """ + Iterate over examples in the split. + + :yields Example: The next example in the split + """ + for i in range(len(self)): + yield self[i] + + def __repr__(self) -> str: + """ + Display the sequence as a concise summary. + + :return: String representation + """ + return f"Examples(split={self._split!r}, n={len(self)})" + + @property + def hf(self) -> Dataset: + """ + Get underlying Hugging Face datasets.Dataset (escape hatch). + + :return: Hugging Face dataset + """ + return self._ds + + +class VoiceDataset: + """ + Split aware wrapper around a Hugging Face dataset. + + Provides access to individual splits via indexing or attributes. + + `VoiceDataset["train"]` and `VoiceDataset.train` return a sequence of + `Example` objects (with provenance attached per example). + + :param datasets: Mapping from split to HF dataset for that split + :param spec: Pinned dataset specification. + :param system_col: Column name containing system prompts. + :param question_col: Column name containing question prompts. + :param answer_col: Column name containing answer prompts. + """ + + def __init__( + self, + datasets: Mapping[Split, Dataset], + *, + spec: _PinnedDatasetSpec, + system_col: str = "system", + question_col: str = "question", + answer_col: str = "answer", + ) -> None: + self._spec = spec + self._system_col = system_col + self._question_col = question_col + self._answer_col = answer_col + self._datasets: dict[Split, Dataset] = dict(datasets) + + def __repr__(self) -> str: + """ + Print the object as a nicely presented string. + + :return: String representation of the object. + """ + sizes = {split: len(ds) for split, ds in self._datasets.items()} + return ( + f"{self.__class__.__name__}(" + f"repo_id={self._spec.repo_id!r}, " + f"revision={self._spec.revision!r}, " + f"splits={sizes!r})" + ) + + def __getitem__(self, split: str | Split) -> Sequence[Example]: + """ + Access a dataset split. + + :param split: Split name or Split enum + :return: Sequence of examples with indexed access + """ + s = Split.parse(split) + return _ExampleSeq( + self._datasets[s], + spec=self._spec, + split=s, + system_col=self._system_col, + question_col=self._question_col, + answer_col=self._answer_col, + ) + + def __getattr__(self, name: str) -> Sequence[Example]: + """ + Get dataset split via attribute access. + + :param name: Name of dataset split + :return: Example sequence for requested split + :raises AttributeError: If split does not exist + """ + try: + return self[Split.parse(name)] + except Exception as e: + raise AttributeError(name) from e + + def __len__(self) -> int: + """ + Return the total number of examples in the dataset. + + This counts examples over all splits included in the dataset. + + :return: Number of examples in the dataset + """ + return sum(len(ds) for ds in self._datasets.values()) + + @property + def splits(self) -> tuple[Split, ...]: + """ + Enumerate available dataset splits. + + :return: Available dataset splits + """ + return tuple(self._datasets.keys()) + + @property + def spec(self) -> _PinnedDatasetSpec: + """ + Get (pinned) specification of source dataset. + + :return: Pinned dataset specification + """ + return self._spec diff --git a/src/voice/datasets/get_dataset.py b/src/voice/datasets/get_dataset.py new file mode 100644 index 0000000..fe5fd83 --- /dev/null +++ b/src/voice/datasets/get_dataset.py @@ -0,0 +1,181 @@ +""" +Dataset loading and canonicalisation utilities. + +This module implements the dataset materialisation pipeline +used by the public entrypoint `get_dataset`. + +Responsibilities include: + +- Resolving symbolic revisions (branches/tags) to commit SHAs. +- Loading splits from Hugging Face via `datasets.load_dataset`. +- Canonicalising dataset schemas into a uniform + (system, question, answer) format. +- Validating dataset structure. +- Constructing a split-aware `VoiceDataset` instance. +""" + +from __future__ import annotations + +from datasets import Dataset, load_dataset +from huggingface_hub import HfApi +from huggingface_hub.utils import HfHubHTTPError + +from voice.datasets._schema import Split +from voice.datasets.dataset import ( + DatasetSpec, + VoiceDataset, + _PinnedDatasetSpec, +) + +# ----------------------------------------------------------------------------- +# Utilities (revision resolution, schema canonicalisation .etc) +# ----------------------------------------------------------------------------- + + +def _resolve_revision(repo_id: str, revision: str) -> str: + """ + Resolve a symbolic revision (branch or tag) to a commit SHA. + + :param repo_id: Hugging Face dataset repository id + :param revision: Branch name, tag, or commit hash + :return: Commit SHA corresponding to the revision + :raises RuntimeError: If dataset or revision does not exist + """ + api = HfApi() + + try: + info = api.dataset_info(repo_id=repo_id, revision=revision) + except HfHubHTTPError as e: + raise RuntimeError( + f"Failed to resolve revision '{revision}' for dataset '{repo_id}'." + ) from e + + return str(info.sha) + + +def _extract_chat_style( + column: dict[str, list[dict[str, str]]], +) -> dict[str, str]: + """ + Extract (system, question, answer) dict from chat style example. + + :param column: column containing chat-style messages + :return: canonical dict + """ + messages = column["messages"] + + system = "" + question = "" + answer = "" + + for msg in messages: + role = msg.get("role") + content = msg.get("content", "") + + if role == "system": + system = content + elif role == "user": + question = content + elif role == "assistant": + answer = content + + return { + "system": system, + "question": question, + "answer": answer, + } + + +def _canonicalise_dataset(ds: Dataset) -> Dataset: + """ + Convert a HF dataset into canonical (system, question, answer) format. + + Currently supported schemas: + - Already canonical: columns {system, question, answer} + - Chat style: column 'messages' with list[{role, content}] + + :param ds: Raw Hugging Face dataset + :return: Canonicalised QA dataset + :raises NotImplementedError: If schema not recognised + """ + cols = set(ds.column_names) + + if {"system", "question", "answer"}.issubset(cols): + return ds + + if "messages" in cols and len(cols) == 1: + return ds.map( + _extract_chat_style, + remove_columns=ds.column_names, + ) + + raise NotImplementedError( + f"Unsupported dataset schema. Columns found: {sorted(cols)}" + ) + + +def _validate_canonical(ds: Dataset) -> None: + """ + Ensure dataset contains required canonical columns. + + :param ds: Canonicalised dataset + :return: None + :raises ValueError: If required columns missing + """ + required = {"system", "question", "answer"} + missing = required - set(ds.column_names) + if not required.issubset(ds.column_names): + raise ValueError(f"Missing required columns: {sorted(missing)}") + + +# ----------------------------------------------------------------------------- +# Public API +# ----------------------------------------------------------------------------- + + +def get_dataset(spec: DatasetSpec) -> VoiceDataset: + """ + Materialise a Hugging Face dataset as a VoiceDataset. + + Steps: + 1. Resolve DatasetSpec to _PinnedDatasetSpec. + 2. Load requested splits from Hugging Face. + 3. Canonicalise each split into (system, question, answer). + 4. Validate canonical structure. + 5. Construct VoiceDataset. + + :param spec: Dataset specification + :return: Split-aware VoiceDataset + :raises RuntimeError: If a split cannot be loaded + """ + # Resolve dataset revision + pinned: _PinnedDatasetSpec = spec._pin(_resolve_revision) + + # Load dataset splits + datasets: dict[Split, Dataset] = {} + + for split in pinned.splits: + try: + ds = load_dataset( + pinned.repo_id, + split=split.value, + revision=pinned.revision, + ) + except Exception as e: + raise RuntimeError( + f"Failed to load split '{split.value}' " + f"from {pinned.repo_id}@{pinned.revision}" + ) from e + + # Canonicalise and validate dataset + ds = _canonicalise_dataset(ds) + + _validate_canonical(ds) + + datasets[split] = ds + + # Build VoiceDataset object + return VoiceDataset( + datasets=datasets, + spec=pinned, + ) diff --git a/src/voice/stylometry/__init__.py b/src/voice/stylometry/__init__.py new file mode 100644 index 0000000..bf904f0 --- /dev/null +++ b/src/voice/stylometry/__init__.py @@ -0,0 +1,59 @@ +""" +Stylometry package for VOICE. + +This package contains: +- Functions for calculating common stylometric metrics +- Utilities for comparing distributions of stylometric metrics +""" + +from voice.stylometry.comparison import ( + make_comparison, + stylometric_distribution, +) +from voice.stylometry.metrics import ( + calculate_avg_word_length, + calculate_char_3gram_moving_avg_type_token_ratio, + calculate_char_3gram_type_token_ratio, + calculate_char_4gram_moving_avg_type_token_ratio, + calculate_char_4gram_type_token_ratio, + calculate_char_5gram_moving_avg_type_token_ratio, + calculate_char_5gram_type_token_ratio, + calculate_dis_legomena_ratio, + calculate_function_word_ratio, + calculate_hapax_legomena_ratio, + calculate_kurtosis_word_length, + calculate_moving_avg_type_token_ratio, + calculate_num_words, + calculate_skew_word_length, + calculate_std_word_length, + calculate_tri_legomena_ratio, + calculate_type_token_ratio, + get_metrics, +) +from voice.stylometry.plotting import ( + plot_kde, +) + +__all__: list[str] = [ + "get_metrics", + "calculate_avg_word_length", + "calculate_std_word_length", + "calculate_skew_word_length", + "calculate_kurtosis_word_length", + "calculate_num_words", + "calculate_hapax_legomena_ratio", + "calculate_dis_legomena_ratio", + "calculate_tri_legomena_ratio", + "calculate_function_word_ratio", + "calculate_type_token_ratio", + "calculate_moving_avg_type_token_ratio", + "calculate_char_3gram_type_token_ratio", + "calculate_char_4gram_type_token_ratio", + "calculate_char_5gram_type_token_ratio", + "calculate_char_3gram_moving_avg_type_token_ratio", + "calculate_char_4gram_moving_avg_type_token_ratio", + "calculate_char_5gram_moving_avg_type_token_ratio", + "stylometric_distribution", + "plot_kde", + "make_comparison", +] diff --git a/src/voice/stylometry/_defaults.py b/src/voice/stylometry/_defaults.py new file mode 100644 index 0000000..8db9735 --- /dev/null +++ b/src/voice/stylometry/_defaults.py @@ -0,0 +1,135 @@ +""" +Defaults for the stylometry module. + +This module contains defaults for stylometric analysis +e.g. default parameters for parameterised metrics +""" + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class ParameterDefaults: + """ + Dataclass containing defaults for parameterised stylometric metrics. + + .. attribute :: mattr_window_size + + Default window size for moving average type-token ratio + + .. attribute :: mattr_window_size_char + + Default window size for moving average type-token ratio + on character n-grams + + .. attribute :: mattr_stride + + Default stride for moving average type-token ratio + + .. attribute :: mattr_stride_char + + Default stride for moving average type-token ratio + on character n-grams + """ + + mattr_window_size: int = 50 + mattr_window_size_char: int = 300 + mattr_stride: int = 1 + mattr_stride_char: int = 1 + + +PARAMETER_DEFAULTS: ParameterDefaults = ParameterDefaults() + + +@dataclass(frozen=True) +class CalibrationDefaults: + """ + Default parameters for self-Wasserstein calibration. + + These defaults control the Monte Carlo procedure used to estimate the + distribution of Wasserstein distances between two independent samples + drawn from the reference (training) corpus. + + This distribution serves as a baseline for interpreting observed + model–reference stylometric distances. + + .. attribute :: sample_size + + Number of examples in each resampled subset from the training split. + + .. attribute :: num_iterations + + Number of Monte Carlo resampling iterations used to estimate + the self-Wasserstein distribution. + + .. attribute :: seed + + RNG seed for reproducibility. + """ + + sample_size: int = 100 + num_iterations: int = 10_000 + seed: int = 42 + + +CALIBRATION_DEFAULTS: CalibrationDefaults = CalibrationDefaults() + + +@dataclass(frozen=True) +class ComparisonDefaults: + """ + Default parameters for comparing stylometric distributions. + + .. attribute :: alpha + + Significance level for hypothesis testing. + """ + + alpha: float = 0.05 + + +COMPARISON_DEFAULTS: ComparisonDefaults = ComparisonDefaults() + + +@dataclass(frozen=True) +class PlottingDefaults: + """ + Default parameters for plotting stylometric distributions. + + .. attribute :: colours + + Colour hierarchy for plotting multiple distributions on the + same plot. + + .. attribute :: window + + Default KDE bandwidth adjustment. + + .. attribute :: fill + + Default for whether to fill KDE area. + + .. attribute :: cut + + Default cut parameter to control extent of the KDE curve past + extreme datapoints. + """ + + colours = [ + "#4E79A7", + "#F28E2B", + "#E15759", + "#76B7B2", + "#59A14F", + "#EDC948", + "#B07AA1", + "#FF9DA7", + "#9C755F", + "#BAB0AC", + ] + window = 1.0 + fill = False + cut = 0.0 + + +PLOTTING_DEFAULTS: PlottingDefaults = PlottingDefaults() diff --git a/src/voice/stylometry/_lexicons.py b/src/voice/stylometry/_lexicons.py new file mode 100644 index 0000000..887ac5d --- /dev/null +++ b/src/voice/stylometry/_lexicons.py @@ -0,0 +1,139 @@ +""" +Internal lexicons used by stylometric metrics. + +This package contains private lexicon data that are not part of the public API. +""" + +from __future__ import annotations + +# Canonical English function word lexicon (lowercase only) +# Source: NLTK cf. https://gist.github.com/sebleier/554280 +FUNCTION_WORDS: frozenset[str] = frozenset( + { + "i", + "me", + "my", + "myself", + "we", + "our", + "ours", + "ourselves", + "you", + "your", + "yours", + "yourself", + "yourselves", + "he", + "him", + "his", + "himself", + "she", + "her", + "hers", + "herself", + "it", + "its", + "itself", + "they", + "them", + "their", + "theirs", + "themselves", + "what", + "which", + "who", + "whom", + "this", + "that", + "these", + "those", + "am", + "is", + "are", + "was", + "were", + "be", + "been", + "being", + "have", + "has", + "had", + "having", + "do", + "does", + "did", + "doing", + "a", + "an", + "the", + "and", + "but", + "if", + "or", + "because", + "as", + "until", + "while", + "of", + "at", + "by", + "for", + "with", + "about", + "against", + "between", + "into", + "through", + "during", + "before", + "after", + "above", + "below", + "to", + "from", + "up", + "down", + "in", + "out", + "on", + "off", + "over", + "under", + "again", + "further", + "then", + "once", + "here", + "there", + "when", + "where", + "why", + "how", + "all", + "any", + "both", + "each", + "few", + "more", + "most", + "other", + "some", + "such", + "no", + "nor", + "not", + "only", + "own", + "same", + "so", + "than", + "too", + "very", + "can", + "will", + "just", + "don", + "should", + "now", + } +) diff --git a/src/voice/stylometry/_sequence_stats.py b/src/voice/stylometry/_sequence_stats.py new file mode 100644 index 0000000..8b98df6 --- /dev/null +++ b/src/voice/stylometry/_sequence_stats.py @@ -0,0 +1,108 @@ +""" +Internal sequence statistics utilities. + +This module provides lightweight helpers for stylometric metrics that operate +over a pre-tokenised sequence of units (e.g. word tokens, character n-grams). +It is private by convention and not part of the public API. +""" + +from __future__ import annotations + +from collections.abc import Sequence + + +def length_central_moment_from_units( + units: Sequence[str], *, order: int +) -> float: + """ + Calculate the central moment for lengths of a sequence of units. + + This sequence must be pre-tokenized. + + Conventions: + - Uses population moments (divide by n). + - Central moment of an empty sequence is 0.0. + - If order is not positive, raises ValueError. + + :param units: Sequence of units + :param order: Central moment order (1, 2, 3, ...) + :return: Central moment of unit lengths as a float + :raises ValueError: If order is not positive + """ + if order <= 0: + raise ValueError("Order must be positive") + + if not units: + return 0.0 + + n = len(units) + mean_len = sum(len(u) for u in units) / n + return sum((len(u) - mean_len) ** order for u in units) / n + + +def ttr_from_units(units: Sequence[str]) -> float: + """ + Calculate the type-token ratio (TTR) of a sequence. + + The sequence must be pre-tokenized + + TTR is the number of unique types divided by the total number of tokens. + + TTR of an empty sequence is 0.0. + + :param units: Token sequence (e.g. words, character n-grams) + :return: Type-token ratio as a float + """ + if not units: + return 0.0 + return len(set(units)) / len(units) + + +def mattr_from_units( + units: Sequence[str], *, window_size: int, stride: int +) -> float: + """ + Calculate the moving average type-token ratio (MATTR) of a sequence. + + The sequence must be pre-tokenized. + + MATTR is the average of TTR computed over a sliding window of fixed size. + + MATTR of an empty sequence is 0.0. + + Notes: + - Uses all contiguous windows of fixed size with fixed stride. + - If the sequence has fewer tokens than window_size, we fall back to + TTR over the whole sequence. + + :param units: Token sequence (e.g. words, character n-grams) + :param window_size: Sliding window size in tokens + :param stride: Stride between consecutive windows + :return: MATTR as a float + :raises ValueError: If window_size or stride is not positive + """ + if window_size <= 0: + raise ValueError("window_size must be positive") + + if stride <= 0: + raise ValueError("stride must be positive") + + if not units: + return 0.0 + + n = len(units) + if n <= window_size: + # Fall back to TTR on the whole sequence + return ttr_from_units(units) + + last_start = n - window_size + starts = range(0, last_start + 1, stride) + + total = 0.0 + count = 0 + for i in starts: + window = units[i : i + window_size] + total += len(set(window)) / window_size + count += 1 + + return total / count diff --git a/src/voice/stylometry/_tokenizers.py b/src/voice/stylometry/_tokenizers.py new file mode 100644 index 0000000..0390abf --- /dev/null +++ b/src/voice/stylometry/_tokenizers.py @@ -0,0 +1,64 @@ +""" +Internal text tokenizers. + +This module provides lightweight tokenizers intended for stylometric analysis. +It is private by convention and not part of the public API. + +- `word_tokenize` tokenizes text into word-level tokens suitable for + lexeme-based stylometric metrics. + +- `char_tokenize` tokenizes text into overlapping character n-grams suitable + for character-level stylometric metrics. +""" + +from __future__ import annotations + +import re + +_TOKEN_RE: re.Pattern[str] = re.compile(r"[a-z]+(?:'[a-z]+)*") + + +def word_tokenize(text: str) -> list[str]: + """ + Tokenize a piece of text into a list of word tokens. + + Tokenization rules: + - The text is lowercased. + - Tokens are contiguous sequences of ASCII letters 'a' to 'z'. + - Tokens may contain internal apostrophes separating letter sequences + - All other characters (whitespace, punctuation, digits, symbols) are + treated as delimiters. + + :param text: Provided text + :return: List of lowercase tokens + """ + return _TOKEN_RE.findall(text.lower()) + + +def char_tokenize(text: str, *, n: int) -> list[str]: + """ + Tokenize a piece of text into overlapping character n-grams. + + Tokenization rules: + - The text is lowercased. + - Whitespace is normalised to a single space. + - Punctuation and symbols are preserved. + - No padding is applied at text boundaries. + - If the normalised text is shorter than n characters, an empty list + is returned. + + :param text: Provided text + :param n: Character n-gram order (must be positive) + :return: List of overlapping character n-grams + :raises ValueError: If n is not positive + """ + if n <= 0: + raise ValueError("n must be positive") + + # Lowercase and normalise whitespace + normalised = " ".join(text.lower().split()) + + if len(normalised) < n: + return [] + + return [normalised[i : i + n] for i in range(len(normalised) - n + 1)] diff --git a/src/voice/stylometry/comparison.py b/src/voice/stylometry/comparison.py new file mode 100644 index 0000000..9a5b55e --- /dev/null +++ b/src/voice/stylometry/comparison.py @@ -0,0 +1,368 @@ +""" +Utilities for comparing stylometric distributions. + +This module contains: + - Extraction of stylometric distributions from datasets + - Self-Wasserstein calibration distribution (train/train resampling) + - Empirical CDF based calibrated percentile + +All calibration is performed relative to the training split by design, +to avoid data leakage from validation/test splits. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from dataclasses import dataclass, field + +import numpy as np +from scipy.stats import wasserstein_distance + +from voice.stylometry.metrics import get_metrics +from voice.datasets import VoiceDataset +from voice.datasets.dataset import Example +from voice.stylometry._defaults import ( + CALIBRATION_DEFAULTS, + COMPARISON_DEFAULTS, +) + +# ----------------------------------------------------------------------------- +# Distribution extraction +# ----------------------------------------------------------------------------- + + +def stylometric_distribution(ds: Sequence[Example], metric: str) -> np.ndarray: + """ + Compute the stylometric distribution for a given metric over a split. + + :param ds: Sequence of examples, each expected to have an `answer` field + :param metric: Name of a registered stylometric metric + :return: 1D numpy array of metric values (dtype=float) + :raises ValueError: If `metric` is not registered + """ + fn = get_metrics().get(metric) + if fn is None: + raise ValueError( + f"{metric} is not a valid metric.\n" + f"Available metrics: {', '.join(get_metrics().keys())}" + ) + + return np.array([fn.fn(e.answer) for e in ds], dtype=float) + + +# ----------------------------------------------------------------------------- +# Calibration: self-Wasserstein distribution +# ----------------------------------------------------------------------------- + + +def self_wasserstein_distribution( + ds: VoiceDataset, + *, + metric: str, + sample_size: int = CALIBRATION_DEFAULTS.sample_size, + num_iterations: int = CALIBRATION_DEFAULTS.num_iterations, + seed: int = CALIBRATION_DEFAULTS.seed, +) -> np.ndarray: + """ + Estimate the self-Wasserstein distribution for a metric via resampling. + + For each iteration, we draw 2*`sample_size` unique examples from the + training split, split them into two non-overlapping groups of size + `sample_size`, and compute the Wasserstein distance between the resulting + empirical distributions of metric values. + + :param ds: A VoiceDataset providing a `train` split + :param metric: Name of a registered stylometric metric + :param sample_size: Size of each resampled subset + :param num_iterations: Number of Monte Carlo resampling iterations + :param seed: RNG seed for reproducibility. + :return: array of Wasserstein distances of length `num_iterations` + :raises ValueError: If inputs are invalid or metric is not registered + """ + metrics = get_metrics() + if metric not in metrics: + raise ValueError( + f"{metric} is not a valid metric.\n" + f"Available metrics: {', '.join(metrics.keys())}" + ) + + ds_train = ds.train + n_train = len(ds_train) + + if sample_size <= 0: + raise ValueError("sample_size must be a positive integer.") + if num_iterations <= 0: + raise ValueError("num_iterations must be a positive integer.") + if 2 * sample_size > n_train: + raise ValueError( + f"Sample size must be <= half the training set size " + f"({n_train / 2})." + ) + + values = stylometric_distribution(ds_train, metric) + + rng = np.random.default_rng(seed) + out = np.empty(num_iterations, dtype=float) + + for i in range(num_iterations): + idx = rng.choice(n_train, size=2 * sample_size, replace=False) + a = values[idx[:sample_size]] + b = values[idx[sample_size:]] + out[i] = wasserstein_distance(a, b) + + return out + + +# ----------------------------------------------------------------------------- +# Calibrated percentile +# ----------------------------------------------------------------------------- + + +def calibrated_percentile(value: float, dist: np.ndarray) -> float: + """ + Compute the calibrated percentile (empirical CDF) of `value` under `dist`. + + This returns: + p = (1/N) * sum_i 1 * [dist_i <= value] + + :param value: Scalar value to evaluate + :param dist: 1D array representing a reference distribution + :return: Empirical CDF value in [0, 1] + :raises ValueError: If `dist` is empty + """ + dist = np.asarray(dist, dtype=float).ravel() + if dist.size == 0: + raise ValueError("dist must be non-empty.") + return float(np.mean(dist <= value)) + + +# ----------------------------------------------------------------------------- +# Comparison results container +# ----------------------------------------------------------------------------- + + +@dataclass(frozen=True, slots=True) +class ComparisonEntry: + """ + Result for a single stylometric metric comparison. + + .. attribute :: metric + + Name of the metric. + + .. attribute :: wasserstein + + Observed Wasserstein distance between model and reference. + + .. attribute :: percentile + + Calibrated percentile under the self-Wasserstein distribution. + + .. attribute :: tail + + Right-tail extremeness (1 - percentile). + + .. attribute :: flag + + Boolean indicating percentile >= 1 - alpha. + + .. attribute :: flag_bonf + + Boolean indicating percentile >= 1 - alpha / N, + where N is the number of metrics in the comparison. + """ + + metric: str + wasserstein: float + percentile: float + tail: float + flag: bool + flag_bonf: bool + + +@dataclass(slots=True) +class ComparisonResults: + """ + Container for stylometric comparison results across multiple metrics. + + The object is initialised with a subset of metric names and a + significance level `alpha`. Entries can then be added for each metric. + + Bonferroni correction is computed automatically using the total + number of metrics stored in this object. + + .. attribute :: metrics + + List of stylometric metrics to run comparison for. + + .. attribute :: alpha + + Significance level for hypothesis testing. + """ + + metrics: tuple[str, ...] + alpha: float = COMPARISON_DEFAULTS.alpha + _entries: dict[str, ComparisonEntry] = field(default_factory=dict) + + def __post_init__(self) -> None: + """ + Validate provided metrics. + + :return: None + :raises ValueError: If any `metrics` are unknown + """ + registry = get_metrics() + unknown = [m for m in self.metrics if m not in registry] + if unknown: + raise ValueError( + f"Unknown metric(s): {', '.join(unknown)}.\n" + f"Available metrics: {', '.join(registry.keys())}" + ) + + def __repr__(self) -> str: + """ + Represent results as a simple metric -> entry mapping. + + :return: A string representation of the results + """ + return repr(self.as_dict()) + + @property + def num_metrics(self) -> int: + """Number of metrics in this comparison.""" + return len(self.metrics) + + @property + def bonf_threshold(self) -> float: + """Percentile threshold for Bonferroni-corrected significance.""" + return 1.0 - self.alpha / self.num_metrics + + @property + def non_significant(self) -> tuple[str, ...]: + """Metric names where both flag and flag_bonf are False.""" + return tuple( + m + for m in self.metrics + if m in self._entries + and not self._entries[m].flag + and not self._entries[m].flag_bonf + ) + + def add( + self, + *, + metric: str, + wasserstein: float, + percentile: float, + ) -> None: + """ + Add a comparison result for a metric. + + :param metric: Metric name + :param wasserstein: Observed Wasserstein distance + :param percentile: Calibrated percentile + :return: None + :raises ValueError: If `metric` not initialised with class + """ + if metric not in self.metrics: + raise ValueError( + f"Metric '{metric}' not initialised in this ComparisonResults." + ) + + tail = 1.0 - percentile + flag = percentile >= (1.0 - self.alpha) + flag_bonf = percentile >= self.bonf_threshold + + entry = ComparisonEntry( + metric=metric, + wasserstein=float(wasserstein), + percentile=float(percentile), + tail=float(tail), + flag=flag, + flag_bonf=flag_bonf, + ) + + self._entries[metric] = entry + + def get(self, metric: str) -> ComparisonEntry: + """ + Retrieve the result entry for a metric. + + :param metric: Metric name + :return: ComparisonEntry for metric + """ + return self._entries[metric] + + def as_dict(self) -> dict[str, ComparisonEntry]: + """ + Return a mapping of metric name to ComparisonEntry. + + :return: mapping of metric name to ComparisonEntry object + """ + return dict(self._entries) + + +# ----------------------------------------------------------------------------- +# Comparison creation +# ----------------------------------------------------------------------------- + + +def make_comparison( + completions: Sequence[Example], + true_ds: VoiceDataset, + *, + metrics: Sequence[str] | None = None, +) -> ComparisonResults: + """ + Compare stylometric distributions of `completions` to the true corpus. + + For each metric, we compute: + - Observed Wasserstein distance between completions and the + reference completions on the same prompts. + - Calibrated percentile of the observed distance under the + self-Wasserstein distribution estimated from the true training split + + The returned ComparisonResults includes per-metric flags at level `alpha` + and Bonferroni-corrected flags across the selected metrics. + + :param completions: Sequence of examples to analyse + :param true_ds: VoiceDataset providing the true corpus + :param metrics: Optional subset of metric names; defaults to all metrics + :return: ComparisonResults populated for the selected metrics + :raises ValueError: + - If completions is empty, + - If sample sizes are invalid + - If metrics are not registered + """ + if not completions: + raise ValueError("completions must be non-empty.") + + registry = get_metrics() + metric_list = list(registry.keys()) if metrics is None else list(metrics) + + results = ComparisonResults(metrics=tuple(metric_list)) + + # Split that observed completions are generated on + observed_split = completions[0].split + ds_observed_split = true_ds[observed_split] + + for metric in metric_list: + # Observed distributions + x = stylometric_distribution(completions, metric) + + # Wasserstein + y = stylometric_distribution(ds_observed_split, metric) + + d = float(wasserstein_distance(x, y)) + + # Self-W calibration distribution + self_dist = self_wasserstein_distribution( + true_ds, + metric=metric, + ) + + p = calibrated_percentile(d, self_dist) + + results.add(metric=metric, wasserstein=d, percentile=p) + + return results diff --git a/src/voice/stylometry/metrics.py b/src/voice/stylometry/metrics.py new file mode 100644 index 0000000..8d0169a --- /dev/null +++ b/src/voice/stylometry/metrics.py @@ -0,0 +1,504 @@ +""" +Stylometric metric suite for analysing 'style' in text. + +All metrics are floats. + +The currently implemented metrics are: + + - Average word length (`avg_word_length`) + - Standard deviation of word length (`std_word_length`) + - Skewness of word length (`skew_word_length`) + - Standard kurtosis of word length (`kurtosis_word_length`) + - Total number of words (`num_words`) + - Hapax legomena ratio (`hapax_legomena_ratio`) + - Dis legomena ratio (`dis_legomena_ratio`) + - Tri legomena ratio (`tri_legomena_ratio`) + - Function word ratio (`function_word_ratio`) + - Type-token ratio (TTR) (`type_token_ratio`) + - Moving average type-token ratio (MATTR) (`moving_avg_type_token_ratio`) + - TTR on character n-grams (`char_*gram_type_token_ratio`) + - MATTR on character n-grams (`moving_avg_char_*gram_type_token_ratio`) + +All metrics are registered to a global registry via a decorator. +The registry can be obtained as a dictionary using get_metrics(). +""" + +from __future__ import annotations + +import math +from collections.abc import Callable +from dataclasses import dataclass + +from voice.stylometry._defaults import PARAMETER_DEFAULTS +from voice.stylometry._lexicons import FUNCTION_WORDS +from voice.stylometry._sequence_stats import ( + length_central_moment_from_units, + mattr_from_units, + ttr_from_units, +) +from voice.stylometry._tokenizers import char_tokenize, word_tokenize + +# ----------------------------------------------------------------------------- +# Registry & metadata +# ----------------------------------------------------------------------------- + + +MetricFn = Callable[[str], float] + + +@dataclass(frozen=True, slots=True) +class MetricSpec: + """ + Dataclass representing a metric specification. + + .. attribute :: fn + + Function mapping a string to a float. + + .. attribute :: description + + Description of the metric. + """ + + fn: MetricFn + description: str | None = None + + def __repr__(self) -> str: + """ + Return a concise string representation of the metric specification. + + :return: Readable string representation of the MetricSpec + """ + fn_name = getattr(self.fn, "__name__", repr(self.fn)) + return f"MetricSpec(fn={fn_name}, description={self.description!r})" + + +_REGISTRY: dict[str, MetricSpec] = {} + + +def metric( + name: str, *, description: str | None = None +) -> Callable[[MetricFn], MetricFn]: + """ + Register a metric function in the global registry. + + Description can also be optionally added. + + :param name: Name of the metric (snake case) + :param description: Description of the metric + :return: Decorator that registers the metric function + """ + + def decorator(fn: MetricFn) -> MetricFn: + """ + Register a metric function in the global registry. + + This function is invoked at decoration time. + + :param fn: Metric function mapping text to a float + :return: The same function, unmodified + :raises ValueError: If a metric function name is already registered + """ + if name in _REGISTRY: + raise ValueError(f"Duplicate metric name '{name}'") + _REGISTRY[name] = MetricSpec(fn=fn, description=description) + return fn + + return decorator + + +def get_metrics() -> dict[str, MetricSpec]: + """ + Return a copy of the metric registry. + + :return: A copy of the metric registry + """ + return dict(_REGISTRY) + + +# ----------------------------------------------------------------------------- +# Word length moment metrics +# ----------------------------------------------------------------------------- + + +@metric("avg_word_length", description="Average word length") +def calculate_avg_word_length(text: str) -> float: + """ + Calculate the average word length of a string. + + Average word length of an empty string is 0.0. + + :param text: Provided text + :return: Average word length as a float + """ + words = word_tokenize(text) + if not words: + return 0.0 + return sum(len(word) for word in words) / len(words) + + +@metric("std_word_length", description="Standard deviation of word length") +def calculate_std_word_length(text: str) -> float: + """ + Calculate the standard deviation of word length in a string. + + Standard deviation of word length for an empty string is 0.0. + + :param text: Provided text + :return: Standard deviation of word length as a float + """ + words = word_tokenize(text) + return math.sqrt(length_central_moment_from_units(words, order=2)) + + +@metric("skew_word_length", description="Skewness of word length") +def calculate_skew_word_length(text: str) -> float: + """ + Calculate the skewness of word length in a string. + + Skewness of word length for an empty string is 0.0. + + If all word lengths are equal, skewness is 0.0. + + :param text: Provided text + :return: Skewness of word length as a float + """ + words = word_tokenize(text) + m2 = length_central_moment_from_units(words, order=2) + if m2 == 0.0: + return 0.0 + m3 = length_central_moment_from_units(words, order=3) + return float(m3 / (m2**1.5)) + + +@metric("kurtosis_word_length", description="Kurtosis of word length") +def calculate_kurtosis_word_length(text: str) -> float: + """ + Calculate the kurtosis of word length in a string. + + Kurtosis of word length for an empty string is 0.0. + + If all word lengths are equal, kurtosis is 0.0. + + Note: This returns the standard kurtosis, not excess kurtosis + i.e. the standard kurtosis of the normal distribution is 3. + + :param text: Provided text + :return: Kurtosis of word length as a float + """ + words = word_tokenize(text) + m2 = length_central_moment_from_units(words, order=2) + if m2 == 0.0: + return 0.0 + m4 = length_central_moment_from_units(words, order=4) + return m4 / (m2**2) + + +# ----------------------------------------------------------------------------- +# Count and legomena ratio metrics +# ----------------------------------------------------------------------------- + + +@metric("num_words", description="Number of words in text") +def calculate_num_words(text: str) -> float: + """ + Calculate the number of words in a string. + + Number of words in an empty string is 0.0. + + :param text: Provided text + :return: Number of words as a float + """ + return float(len(word_tokenize(text))) + + +@metric("hapax_legomena_ratio", description="Ratio of hapax legomena") +def calculate_hapax_legomena_ratio(text: str) -> float: + """ + Calculate the ratio of hapax legomena in a string. + + A hapax legomenon is a word the appears once in a piece of text. + + Hapax legomena ratio of an empty string is 0.0. + + :param text: Provided text + :return: Hapax legomena ratio as a float + """ + words = word_tokenize(text) + if not words: + return 0.0 + + counts: dict[str, int] = {} + for w in words: + counts[w] = counts.get(w, 0) + 1 + + hapax = sum(1 for c in counts.values() if c == 1) + return hapax / len(words) + + +@metric("dis_legomena_ratio", description="Ratio of dis legomena") +def calculate_dis_legomena_ratio(text: str) -> float: + """ + Calculate the ratio of dis legomena in a string. + + A dis legomenon is a word the appears twice in a piece of text. + + Dis legomena ratio of an empty string is 0.0. + + :param text: Provided text + :return: Dis legomena ratio as a float + """ + words = word_tokenize(text) + if not words: + return 0.0 + + counts: dict[str, int] = {} + for w in words: + counts[w] = counts.get(w, 0) + 1 + + dis = sum(1 for c in counts.values() if c == 2) + return dis / len(words) + + +@metric("tri_legomena_ratio", description="Ratio of tri legomena") +def calculate_tri_legomena_ratio(text: str) -> float: + """ + Calculate the ratio of tri legomena in a string. + + A tri legomenon is a word the appears thrice in a piece of text. + + Tri legomena ratio of an empty string is 0.0. + + :param text: Provided text + :return: Tri legomena ratio as a float + """ + words = word_tokenize(text) + if not words: + return 0.0 + + counts: dict[str, int] = {} + for w in words: + counts[w] = counts.get(w, 0) + 1 + + tri = sum(1 for c in counts.values() if c == 3) + return tri / len(words) + + +@metric("function_word_ratio", description="Ratio of function words") +def calculate_function_word_ratio(text: str) -> float: + """ + Calculate the ratio of function words in a string. + + A function word is a word whose purpose is to contribute to the syntax + rather than the meaning of a sentence. + + Function word frequency of an empty string is 0.0. + + :param text: Provided text + :return: Function word ratio as a float + """ + words = word_tokenize(text) + if not words: + return 0.0 + + return sum(1 for word in words if word in FUNCTION_WORDS) / len(words) + + +# ----------------------------------------------------------------------------- +# Word TTR / MATTR metrics +# ----------------------------------------------------------------------------- + + +@metric("type_token_ratio", description="Type-token ratio") +def calculate_type_token_ratio(text: str) -> float: + """ + Calculate the type-token ratio (TTR) of a string. + + TTR is the number of unique word types divided by + the total number of tokens. + + TTR of an empty string is 0.0. + + :param text: Provided text + :return: Type-token ratio as a float + """ + return ttr_from_units(word_tokenize(text)) + + +@metric( + "moving_avg_type_token_ratio", + description="Moving average type-token ratio", +) +def calculate_moving_avg_type_token_ratio(text: str) -> float: + """ + Calculate the moving average type-token ratio (MATTR) of a string. + + MATTR is the average of TTR computed over a sliding window of fixed size. + + MATTR of an empty string is 0.0. + + Notes: + - Uses all contiguous windows of fixed size with fixed stride. The defaults + for these parameters are stored in PARAMETER_DEFAULTS. + - If the text has fewer tokens than window_size, we fall back + to TTR over the whole text. + + :param text: Provided text + :return: MATTR as a float + """ + return mattr_from_units( + word_tokenize(text), + window_size=PARAMETER_DEFAULTS.mattr_window_size, + stride=PARAMETER_DEFAULTS.mattr_stride, + ) + + +# ----------------------------------------------------------------------------- +# Character n-gram TTR metrics (n = 3,4,5) +# ----------------------------------------------------------------------------- + + +@metric( + "char_3gram_type_token_ratio", + description="Type-token ratio of character 3-grams", +) +def calculate_char_3gram_type_token_ratio(text: str) -> float: + """ + Calculate the type-token ratio (TTR) of character 3-grams in a string. + + TTR is the number of unique character 3-grams divided by the total + number of character 3-grams. + + TTR of an empty string is 0.0. + + :param text: Provided text + :return: Character 3-gram type-token ratio as a float + """ + return ttr_from_units(char_tokenize(text, n=3)) + + +@metric( + "char_4gram_type_token_ratio", + description="Type-token ratio of character 4-grams", +) +def calculate_char_4gram_type_token_ratio(text: str) -> float: + """ + Calculate the type-token ratio (TTR) of character 4-grams in a string. + + TTR is the number of unique character 4-grams divided by the total + number of character 4-grams. + + TTR of an empty string is 0.0. + + :param text: Provided text + :return: Character 4-gram type-token ratio as a float + """ + return ttr_from_units(char_tokenize(text, n=4)) + + +@metric( + "char_5gram_type_token_ratio", + description="Type-token ratio of character 5-grams", +) +def calculate_char_5gram_type_token_ratio(text: str) -> float: + """ + Calculate the type-token ratio (TTR) of character 5-grams in a string. + + TTR is the number of unique character 5-grams divided by the total + number of character 5-grams. + + TTR of an empty string is 0.0. + + :param text: Provided text + :return: Character 5-gram type-token ratio as a float + """ + return ttr_from_units(char_tokenize(text, n=5)) + + +# ----------------------------------------------------------------------------- +# Character n-gram MATTR metrics (n = 3,4,5) +# ----------------------------------------------------------------------------- + + +@metric( + "char_3gram_moving_avg_type_token_ratio", + description="Moving average type-token ratio of character 3-grams", +) +def calculate_char_3gram_moving_avg_type_token_ratio(text: str) -> float: + """ + Calculate the moving average type-token ratio (MATTR) of character 3-grams. + + MATTR is the average of TTR computed over a sliding window of fixed size. + + MATTR of an empty string is 0.0. + + Notes: + - Uses all contiguous windows of fixed size with fixed stride. The defaults + for these parameters are stored in PARAMETER_DEFAULTS. + - If the text has fewer n-grams than window_size, we fall back to + TTR over the whole text. + + :param text: Provided text + :return: Character 3-gram MATTR as a float + """ + return mattr_from_units( + char_tokenize(text, n=3), + window_size=PARAMETER_DEFAULTS.mattr_window_size_char, + stride=PARAMETER_DEFAULTS.mattr_stride_char, + ) + + +@metric( + "char_4gram_moving_avg_type_token_ratio", + description="Moving average type-token ratio of character 4-grams", +) +def calculate_char_4gram_moving_avg_type_token_ratio(text: str) -> float: + """ + Calculate the moving average type-token ratio (MATTR) of character 4-grams. + + MATTR is the average of TTR computed over a sliding window of fixed size. + + MATTR of an empty string is 0.0. + + Notes: + - Uses all contiguous windows of fixed size with fixed stride. The defaults + for these parameters are stored in PARAMETER_DEFAULTS. + - If the text has fewer n-grams than window_size, we fall back to + TTR over the whole text. + + :param text: Provided text + :return: Character 4-gram MATTR as a float + """ + return mattr_from_units( + char_tokenize(text, n=4), + window_size=PARAMETER_DEFAULTS.mattr_window_size_char, + stride=PARAMETER_DEFAULTS.mattr_stride_char, + ) + + +@metric( + "char_5gram_moving_avg_type_token_ratio", + description="Moving average type-token ratio of character 5-grams", +) +def calculate_char_5gram_moving_avg_type_token_ratio(text: str) -> float: + """ + Calculate the moving average type-token ratio (MATTR) of character 5-grams. + + MATTR is the average of TTR computed over a sliding window of fixed size. + + MATTR of an empty string is 0.0. + + Notes: + - Uses all contiguous windows of fixed size with fixed stride. The defaults + for these parameters are stored in PARAMETER_DEFAULTS. + - If the text has fewer n-grams than window_size, we fall back to + TTR over the whole text. + + :param text: Provided text + :return: Character 5-gram MATTR as a float + """ + return mattr_from_units( + char_tokenize(text, n=5), + window_size=PARAMETER_DEFAULTS.mattr_window_size_char, + stride=PARAMETER_DEFAULTS.mattr_stride_char, + ) diff --git a/src/voice/stylometry/plotting.py b/src/voice/stylometry/plotting.py new file mode 100644 index 0000000..1193fce --- /dev/null +++ b/src/voice/stylometry/plotting.py @@ -0,0 +1,90 @@ +""" +Plotting utilities for stylometric analysis. + +This module is intentionally separated from computational utilities to avoid +introducing plotting dependencies and global style side effects into core code. +""" + +from __future__ import annotations + +from collections.abc import Mapping + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + +from voice.stylometry._defaults import PLOTTING_DEFAULTS + + +def plot_kde( # noqa: CCR001 + data: np.ndarray | Mapping[str, np.ndarray], + x_name: str = "", + *, + window: float = PLOTTING_DEFAULTS.window, + fill: bool = PLOTTING_DEFAULTS.fill, + cut: float = PLOTTING_DEFAULTS.cut, +) -> plt.Figure: + """ + Plot a KDE (PDF estimate) for one or more 1D arrays. + + If `data` is a single array, no legend is shown. + If `data` is a mapping, keys are used as labels and order is preserved. + + :param data: 1D array or mapping of label -> 1D array + :param x_name: x-axis label (e.g. "Average word length") + :param window: Seaborn KDE bandwidth adjustment (bw_adjust) + :param fill: Whether to fill the KDE area + :param cut: Extent to which the KDE is extended past extreme datapoints + :return: (fig, ax) + :raises ValueError: If no finite numeric values are found + """ + if isinstance(data, np.ndarray): + series = [("values", data)] + show_legend = False + else: + series = list(data.items()) + show_legend = len(series) > 1 + + cleaned: list[tuple[str, np.ndarray]] = [] + for label, arr in series: + x = np.asarray(arr).ravel() + x = x[np.isfinite(x)] + if x.size == 0: + continue + cleaned.append((label, x)) + + if not cleaned: + raise ValueError("No finite numeric values found to plot.") + + fig, ax = plt.subplots(figsize=(8, 4.5)) + + colours = PLOTTING_DEFAULTS.colours + + with sns.axes_style("whitegrid"): + for i, (label, x) in enumerate(cleaned): + color = colours[i % len(colours)] + sns.kdeplot( + x=x, + ax=ax, + label=label if show_legend else None, + bw_adjust=window, + fill=fill, + cut=cut, + color=color, + linewidth=2, + ) + + ax.set_xlabel(x_name or "Value") + ax.set_ylabel("Density") + + title = "PDF" + if x_name: + title += f" of {x_name}" + title += f" (KDE, window {window})" + ax.set_title(title) + + if show_legend: + ax.legend(title=None, frameon=True) + + fig.tight_layout() + return fig, ax diff --git a/tests/voice/datasets/test_dataset.py b/tests/voice/datasets/test_dataset.py new file mode 100644 index 0000000..db0e983 --- /dev/null +++ b/tests/voice/datasets/test_dataset.py @@ -0,0 +1,231 @@ +""" +Tests for voice.datasets.dataset. + +Scope: +- DatasetSpec validation/normalisation +- Revision pinning invariants with _PinnedDatasetSpec +- VoiceDataset split access +""" + +from __future__ import annotations + +import dataclasses + +import pytest +from datasets import Dataset + +from voice import DatasetSpec +from voice.datasets import VoiceDataset +from voice.datasets._schema import Split +from voice.datasets.dataset import ( + Example, + _PinnedDatasetSpec, +) + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _canonical_hf_ds(n: int = 3) -> Dataset: + return Dataset.from_dict( + { + "system": [f"s{i}" for i in range(n)], + "question": [f"q{i}" for i in range(n)], + "answer": [f"a{i}" for i in range(n)], + } + ) + + +def _pinned( + *, + repo_id: str = "ns/name", + revision: str = "a1b2c3d", + splits: tuple[Split, ...] = (Split.TRAIN,), +) -> _PinnedDatasetSpec: + return _PinnedDatasetSpec( + repo_id=repo_id, revision=revision, splits=splits + ) + + +# ----------------------------------------------------------------------------- +# DatasetSpec validation & normalisation +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize("repo_id", ["", "abc", "namespace/", "/name", "/"]) +def test_dataset_spec_rejects_bad_repo_id(repo_id: str): + with pytest.raises(ValueError, match=r"`repo_id` must look like"): + DatasetSpec(repo_id=repo_id) + + +def test_dataset_spec_rejects_empty_revision(): + with pytest.raises(ValueError, match=r"`revision` must be non-empty"): + DatasetSpec(repo_id="ns/name", revision="") + + +def test_dataset_spec_rejects_empty_splits(): + with pytest.raises(ValueError, match=r"`splits` must be non-empty"): + DatasetSpec(repo_id="ns/name", splits=()) + + +def test_dataset_spec_normalises_splits_from_str(): + spec = DatasetSpec(repo_id="ns/name", splits="train") + assert spec.splits == (Split.TRAIN,) + + +def test_dataset_spec_normalises_splits_from_enum(): + spec = DatasetSpec(repo_id="ns/name", splits=Split.TEST) + assert spec.splits == (Split.TEST,) + + +def test_dataset_spec_normalises_splits_from_iterable(): + spec = DatasetSpec(repo_id="ns/name", splits=("train", Split.TEST)) + assert spec.splits == (Split.TRAIN, Split.TEST) + + +# ----------------------------------------------------------------------------- +# Pinned spec invariants & pinning behaviour +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "sha", + [ + "a1b2c3d", + "0123456789abcdef0123456789abcdef01234567", # pragma: allowlist secret + ], +) +def test_pinned_dataset_spec_accepts_sha_like_revisions(sha: str): + p = _PinnedDatasetSpec( + repo_id="ns/name", revision=sha, splits=(Split.TRAIN,) + ) + assert p.revision == sha + + +@pytest.mark.parametrize( + "bad_rev", ["", "zzzzzzz", "123456", "g" * 7, "a" * 41] +) +def test_pinned_dataset_spec_rejects_non_sha_revisions(bad_rev: str): + with pytest.raises(ValueError, match=r"`revision` must be a git SHA"): + _PinnedDatasetSpec( + repo_id="ns/name", revision=bad_rev, splits=(Split.TRAIN,) + ) + + +def test_pinned_dataset_spec_from_spec_calls_resolver_only_when_needed(): + calls: list[tuple[str, str]] = [] + + def resolver(repo_id: str, revision: str) -> str: + calls.append((repo_id, revision)) + return "deadbeef" + + spec1 = DatasetSpec(repo_id="ns/name", revision="main", splits=("train",)) + pinned1 = _PinnedDatasetSpec.from_spec(spec1, resolver) + assert pinned1.revision == "deadbeef" + assert calls == [("ns/name", "main")] + + calls.clear() + spec2 = DatasetSpec( + repo_id="ns/name", revision="a1b2c3d", splits=("train",) + ) + pinned2 = _PinnedDatasetSpec.from_spec(spec2, resolver) + assert pinned2.revision == "a1b2c3d" + assert calls == [] + + +def test_pinned_dataset_spec_repr_looks_like_dataset_spec_repr(): + p = _pinned(repo_id="ns/name", revision="a1b2c3d", splits=(Split.TRAIN,)) + r = repr(p) + assert r.startswith("DatasetSpec(") + assert "repo_id='ns/name'" in r + assert "revision='a1b2c3d'" in r + + +# ----------------------------------------------------------------------------- +# Example ergonomics +# ----------------------------------------------------------------------------- + + +def test_example_getitem_and_provenance(): + p = _pinned() + ex = Example( + system="s", + question="q", + answer="a", + spec=p, + split=Split.TRAIN, + ) + + assert ex["system"] == "s" + assert ex["question"] == "q" + assert ex["answer"] == "a" + with pytest.raises(KeyError): + _ = ex["nope"] + + prov = ex.provenance + assert prov.spec == p + assert prov.split == Split.TRAIN + + +def test_example_is_frozen(): + p = _pinned() + ex = Example( + system="s", question="q", answer="a", spec=p, split=Split.TRAIN + ) + assert type(ex).__dataclass_params__.frozen is True + + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(ex, "system", "x") # noqa: B010 + + +# ----------------------------------------------------------------------------- +# VoiceDataset access +# ----------------------------------------------------------------------------- + + +def test_voice_dataset_len_sums_splits_and_exposes_splits(): + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + vd = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds(2), + Split.TEST: _canonical_hf_ds(3), + }, + spec=p, + ) + assert len(vd) == 5 + assert set(vd.splits) == {Split.TRAIN, Split.TEST} + + +def test_voice_dataset_split_access_attaches_provenance(): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds(2)}, + spec=p, + ) + + seq1 = vd["train"] + seq2 = vd.train + + ex = seq1[1] + assert ex.system == "s1" + assert ex.question == "q1" + assert ex.answer == "a1" + assert ex.spec == p + assert ex.split == Split.TRAIN + + assert seq2[0].split == Split.TRAIN + + +def test_voice_dataset_bad_split_item_raises_key_error(): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset(datasets={Split.TRAIN: _canonical_hf_ds(1)}, spec=p) + with pytest.raises(KeyError): + _ = vd["validation"] + + +def test_voice_dataset_bad_split_attr_raises_attribute_error(): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset(datasets={Split.TRAIN: _canonical_hf_ds(1)}, spec=p) + with pytest.raises(AttributeError): + _ = vd.validation diff --git a/tests/voice/datasets/test_get_dataset.py b/tests/voice/datasets/test_get_dataset.py new file mode 100644 index 0000000..2858738 --- /dev/null +++ b/tests/voice/datasets/test_get_dataset.py @@ -0,0 +1,213 @@ +""" +Tests for voice.datasets.get_dataset. + +Scope: +- Revision resolution via Hugging Face Hub API +- Schema canonicalisation for supported dataset formats +- Canonical schema validation +- Public materialisation pipeline (get_dataset) with dependency patching +""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import pytest +import requests +from datasets import Dataset +from huggingface_hub.utils import HfHubHTTPError + +from voice import DatasetSpec +from voice.datasets._schema import Split +from voice.datasets.dataset import _PinnedDatasetSpec + +gd = importlib.import_module("voice.datasets.get_dataset") + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _canonical_hf_ds(n: int = 3) -> Dataset: + return Dataset.from_dict( + { + "system": [f"s{i}" for i in range(n)], + "question": [f"q{i}" for i in range(n)], + "answer": [f"a{i}" for i in range(n)], + } + ) + + +def _chat_style_hf_ds() -> Dataset: + return Dataset.from_dict( + { + "messages": [ + [ + {"role": "system", "content": "sys"}, + {"role": "user", "content": "q"}, + {"role": "assistant", "content": "a"}, + ], + [ + {"role": "user", "content": "q2"}, + {"role": "assistant", "content": "a2"}, + ], + ] + } + ) + + +# ----------------------------------------------------------------------------- +# Revision resolution +# ----------------------------------------------------------------------------- + + +def test_resolve_revision_returns_info_sha(monkeypatch: pytest.MonkeyPatch): + class FakeApi: + def dataset_info(self, *, repo_id: str, revision: str): + assert repo_id == "ns/name" + assert revision == "main" + return SimpleNamespace(sha="deadbeef") + + monkeypatch.setattr(gd, "HfApi", lambda: FakeApi()) + + assert gd._resolve_revision("ns/name", "main") == "deadbeef" + + +def test_resolve_revision_raises_runtime_error_on_hub_error( + monkeypatch: pytest.MonkeyPatch, +): + class FakeApi: + def dataset_info(self, *, repo_id: str, revision: str): + _ = repo_id + __ = revision + resp = requests.Response() + resp.status_code = 404 + resp.url = "https://huggingface.co" + resp.request = requests.Request("GET", resp.url).prepare() + raise HfHubHTTPError("nope", response=resp) + + monkeypatch.setattr(gd, "HfApi", lambda: FakeApi()) + + with pytest.raises( + RuntimeError, match=r"Failed to resolve revision 'main'" + ): + gd._resolve_revision("ns/name", "main") + + +# ----------------------------------------------------------------------------- +# Canonicalisation & validation +# ----------------------------------------------------------------------------- + + +def test_canonicalise_dataset_passthrough_for_canonical_schema(): + ds = _canonical_hf_ds(2) + out = gd._canonicalise_dataset(ds) + assert out is ds + + +def test_canonicalise_dataset_converts_chat_style_messages_column(): + ds = _chat_style_hf_ds() + out = gd._canonicalise_dataset(ds) + + assert set(out.column_names) == {"system", "question", "answer"} + row0 = out[0] + assert row0["system"] == "sys" + assert row0["question"] == "q" + assert row0["answer"] == "a" + + row1 = out[1] + assert row1["system"] == "" + assert row1["question"] == "q2" + assert row1["answer"] == "a2" + + +def test_canonicalise_dataset_rejects_unknown_schema(): + ds = Dataset.from_dict({"prompt": ["x"], "completion": ["y"]}) + with pytest.raises( + NotImplementedError, match=r"Unsupported dataset schema" + ): + _ = gd._canonicalise_dataset(ds) + + +def test_validate_canonical_accepts_required_columns(): + ds = _canonical_hf_ds(1) + gd._validate_canonical(ds) + + +def test_validate_canonical_raises_on_missing_columns(): + ds = Dataset.from_dict({"system": ["s"], "question": ["q"]}) + with pytest.raises(ValueError, match=r"Missing required columns"): + gd._validate_canonical(ds) + + +# ----------------------------------------------------------------------------- +# get_dataset pipeline +# ----------------------------------------------------------------------------- + + +def test_get_dataset_loads_splits_canonicalises_and_returns_voice_dataset( + monkeypatch: pytest.MonkeyPatch, +): + calls: dict[str, list] = {"load": []} + + def fake_resolve(repo_id: str, revision: str) -> str: + assert repo_id == "ns/name" + assert revision == "main" + return "a1b2c3d" + + def fake_load_dataset(repo_id: str, *, split: str, revision: str): + calls["load"].append((repo_id, split, revision)) + if split == "train": + return _chat_style_hf_ds() + if split == "test": + return _canonical_hf_ds(1) + raise AssertionError("unexpected split") + + monkeypatch.setattr(gd, "_resolve_revision", fake_resolve) + monkeypatch.setattr(gd, "load_dataset", fake_load_dataset) + + spec = DatasetSpec( + repo_id="ns/name", revision="main", splits=("train", "test") + ) + vd = gd.get_dataset(spec) + + assert isinstance(vd.spec, _PinnedDatasetSpec) + assert vd.spec.repo_id == "ns/name" + assert vd.spec.revision == "a1b2c3d" + assert set(vd.splits) == {Split.TRAIN, Split.TEST} + + assert calls["load"] == [ + ("ns/name", "train", "a1b2c3d"), + ("ns/name", "test", "a1b2c3d"), + ] + + ex0 = vd.train[0] + assert ex0.system == "sys" + assert ex0.question == "q" + assert ex0.answer == "a" + assert ex0.split == Split.TRAIN + assert ex0.spec == vd.spec + + +def test_get_dataset_wraps_load_failures_in_runtime_error( + monkeypatch: pytest.MonkeyPatch, +): + def fake_resolve(repo_id: str, revision: str) -> str: + _ = repo_id + __ = revision + return "a1b2c3d" + + def fake_load_dataset(repo_id: str, *, split: str, revision: str): + _ = repo_id + __ = split + ___ = revision + raise RuntimeError("boom") + + monkeypatch.setattr(gd, "_resolve_revision", fake_resolve) + monkeypatch.setattr(gd, "load_dataset", fake_load_dataset) + + spec = DatasetSpec(repo_id="ns/name", revision="main", splits=("train",)) + + with pytest.raises(RuntimeError, match=r"Failed to load split 'train'"): + _ = gd.get_dataset(spec) diff --git a/tests/voice/stylometry/test_comparison.py b/tests/voice/stylometry/test_comparison.py new file mode 100644 index 0000000..3fa307a --- /dev/null +++ b/tests/voice/stylometry/test_comparison.py @@ -0,0 +1,450 @@ +""" +Tests for voice.stylometry.comparison. + +Scope: +- Distribution extraction +- Self-Wasserstein calibration distribution +- Empirical CDF calibrated percentile +- ComparisonResults / ComparisonEntry ergonomics + invariants +- Comparison creation +""" + +from __future__ import annotations + +import dataclasses +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import pytest +from datasets import Dataset + +from voice.datasets import VoiceDataset +from voice.datasets._schema import Split +from voice.datasets.dataset import Example, _PinnedDatasetSpec +from voice.stylometry.comparison import ( + ComparisonEntry, + ComparisonResults, + calibrated_percentile, + make_comparison, + self_wasserstein_distribution, + stylometric_distribution, +) + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _canonical_hf_ds(answers: list[str]) -> Dataset: + n = len(answers) + return Dataset.from_dict( + { + "system": [f"s{i}" for i in range(n)], + "question": [f"q{i}" for i in range(n)], + "answer": list(answers), + } + ) + + +def _pinned( + *, + repo_id: str = "ns/name", + revision: str = "a1b2c3d", + splits: tuple[Split, ...] = (Split.TRAIN,), +) -> _PinnedDatasetSpec: + return _PinnedDatasetSpec( + repo_id=repo_id, revision=revision, splits=splits + ) + + +def _ex( + *, + answer: str, + split: Split = Split.TRAIN, +) -> Example: + return Example( + system="s", + question="q", + answer=answer, + spec=_pinned(), + split=split, + ) + + +@dataclass(frozen=True, slots=True) +class _Metric: + fn: Callable[[str], float] + + +@pytest.fixture() +def metric_registry(): + return { + "len": _Metric(fn=lambda s: float(len(s))), + "vowels": _Metric( + fn=lambda s: float(sum(c in "aeiou" for c in s.lower())) + ), + } + + +@pytest.fixture() +def patch_metrics(monkeypatch, metric_registry): + import voice.stylometry.comparison as comparison + + monkeypatch.setattr( + comparison, "get_metrics", lambda: dict(metric_registry) + ) + return metric_registry + + +# ----------------------------------------------------------------------------- +# stylometric_distribution +# ----------------------------------------------------------------------------- + + +def test_stylometric_distribution_rejects_unknown_metric(patch_metrics): + ds = [_ex(answer="abc")] + with pytest.raises(ValueError, match=r"is not a valid metric"): + stylometric_distribution(ds, "nope") + + +def test_stylometric_distribution_returns_float_array_and_preserves_order( + patch_metrics, +): + ds = [_ex(answer="a"), _ex(answer="abcd"), _ex(answer="xy")] + out = stylometric_distribution(ds, "len") + + assert isinstance(out, np.ndarray) + assert out.dtype == float + assert out.shape == (3,) + assert out.tolist() == [1.0, 4.0, 2.0] + + +# ----------------------------------------------------------------------------- +# calibrated_percentile +# ----------------------------------------------------------------------------- + + +def test_calibrated_percentile_rejects_empty_dist(): + with pytest.raises(ValueError, match=r"dist must be non-empty"): + calibrated_percentile(0.0, np.array([])) + + +@pytest.mark.parametrize( + ("value", "dist", "expected"), + [ + (2.0, np.array([0.0, 1.0, 2.0, 3.0]), 0.75), # <= value + (-1.0, np.array([0.0, 1.0]), 0.0), + (10.0, np.array([0.0, 1.0]), 1.0), + ], +) +def test_calibrated_percentile_basic_correctness(value, dist, expected): + assert calibrated_percentile(value, dist) == expected + + +def test_calibrated_percentile_flattens_non_1d_inputs(): + dist = np.array([[1.0, 2.0], [3.0, 4.0]]) + assert calibrated_percentile(2.0, dist) == 0.5 + + +# ----------------------------------------------------------------------------- +# self_wasserstein_distribution +# ----------------------------------------------------------------------------- + + +def test_self_wasserstein_distribution_rejects_unknown_metric(patch_metrics): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds(["a", "b"])}, spec=p + ) + + with pytest.raises(ValueError, match=r"is not a valid metric"): + self_wasserstein_distribution(vd, metric="nope") + + +@pytest.mark.parametrize( + ("sample_size", "num_iterations", "match"), + [ + (0, 10, r"sample_size must be a positive integer"), + (-1, 10, r"sample_size must be a positive integer"), + (1, 0, r"num_iterations must be a positive integer"), + (1, -5, r"num_iterations must be a positive integer"), + ], +) +def test_self_wasserstein_distribution_validates_simple_inputs( + patch_metrics, + sample_size, + num_iterations, + match, +): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds(["a"] * 10)}, + spec=p, + ) + with pytest.raises(ValueError, match=match): + self_wasserstein_distribution( + vd, + metric="len", + sample_size=sample_size, + num_iterations=num_iterations, + ) + + +def test_self_wasserstein_distribution_rejects_too_large_sample_size( + patch_metrics, +): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds(["a"] * 5)}, + spec=p, + ) + + with pytest.raises(ValueError, match=r"<= half the training set size"): + self_wasserstein_distribution( + vd, metric="len", sample_size=3, num_iterations=1 + ) + + +def test_self_wasserstein_distribution_is_deterministic_given_seed( + patch_metrics, +): + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(20)])}, + spec=p, + ) + + out1 = self_wasserstein_distribution( + vd, metric="len", sample_size=5, num_iterations=25, seed=123 + ) + out2 = self_wasserstein_distribution( + vd, metric="len", sample_size=5, num_iterations=25, seed=123 + ) + + assert out1.shape == (25,) + assert out1.dtype == float + assert np.array_equal(out1, out2) + + out3 = self_wasserstein_distribution( + vd, metric="len", sample_size=5, num_iterations=25, seed=999 + ) + assert not np.array_equal(out1, out3) + + +def test_self_wasserstein_distribution_uses_training_split_only( + patch_metrics, monkeypatch +): + def raising_metric(s: str) -> float: + if s == "BAD": + raise AssertionError("test split should not be touched") + return float(len(s)) + + import voice.stylometry.comparison as comparison + + monkeypatch.setattr( + comparison, + "get_metrics", + lambda: {"len": _Metric(fn=raising_metric)}, + ) + + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + vd = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(20)]), + Split.TEST: _canonical_hf_ds(["BAD"]), + }, + spec=p, + ) + + out = self_wasserstein_distribution( + vd, metric="len", sample_size=5, num_iterations=5 + ) + assert out.shape == (5,) + + +def test_self_wasserstein_distribution_is_zero_for_constant_metric( + patch_metrics, monkeypatch +): + import voice.stylometry.comparison as comparison + + monkeypatch.setattr( + comparison, + "get_metrics", + lambda: {"const": _Metric(fn=lambda _s: 1.0)}, + ) + + p = _pinned(splits=(Split.TRAIN,)) + vd = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(30)])}, + spec=p, + ) + + out = self_wasserstein_distribution( + vd, metric="const", sample_size=10, num_iterations=10 + ) + assert np.allclose(out, 0.0) + + +# ----------------------------------------------------------------------------- +# ComparisonEntry / ComparisonResults +# ----------------------------------------------------------------------------- + + +def test_comparison_entry_is_frozen_and_slots(): + e = ComparisonEntry( + metric="m", + wasserstein=1.0, + percentile=0.9, + tail=0.1, + flag=True, + flag_bonf=False, + ) + assert type(e).__dataclass_params__.frozen is True + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(e, "metric", "x") # noqa: B010 + + +def test_comparison_results_rejects_unknown_metrics(patch_metrics): + with pytest.raises(ValueError, match=r"Unknown metric"): + ComparisonResults(metrics=("len", "nope")) + + +def test_comparison_results_properties_and_add_logic(patch_metrics): + r = ComparisonResults(metrics=("len", "vowels"), alpha=0.1) + + assert r.num_metrics == 2 + assert r.bonf_threshold == 1.0 - 0.1 / 2 + + # Boundary checks + r.add(metric="len", wasserstein=np.float64(1.25), percentile=0.9) + e = r.get("len") + assert e.metric == "len" + assert isinstance(e.wasserstein, float) + assert isinstance(e.percentile, float) + assert e.tail == pytest.approx(0.1) + assert e.flag is True # >= 1 - alpha + assert e.flag_bonf is False # >= 1 - alpha/2 + + r.add(metric="vowels", wasserstein=0.5, percentile=0.0) + e2 = r.get("vowels") + assert e2.flag is False + assert e2.flag_bonf is False + + assert r.non_significant == ("vowels",) + + d = r.as_dict() + d.pop("len") + assert "len" in r.as_dict() + + +def test_comparison_results_add_rejects_metric_not_initialised(patch_metrics): + r = ComparisonResults(metrics=("len",)) + with pytest.raises(ValueError, match=r"not initialised"): + r.add(metric="vowels", wasserstein=0.0, percentile=0.0) + + +# ----------------------------------------------------------------------------- +# make_comparison +# ----------------------------------------------------------------------------- + + +def test_make_comparison_rejects_empty_completions(patch_metrics): + p = _pinned(splits=(Split.TRAIN,)) + true_ds = VoiceDataset( + datasets={Split.TRAIN: _canonical_hf_ds(["a"])}, spec=p + ) + + with pytest.raises(ValueError, match=r"completions must be non-empty"): + make_comparison([], true_ds) + + +def test_make_comparison_defaults_to_registry_metrics(patch_metrics): + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + true_ds = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]), + Split.TEST: _canonical_hf_ds([f"b{i}" for i in range(10)]), + }, + spec=p, + ) + completions = [ + _ex(answer="hello", split=Split.TEST), + _ex(answer="world", split=Split.TEST), + ] + + import voice.stylometry.comparison as comparison + + def fake_self_dist(*_args, **_kwargs): + return np.array([0.0, 1.0, 2.0, 3.0], dtype=float) + + comparison.self_wasserstein_distribution = fake_self_dist # type: ignore[assignment] + + out = make_comparison(completions, true_ds) + + assert out.metrics == ("len", "vowels") + for m in out.metrics: + entry = out.get(m) + assert entry.metric == m + assert 0.0 <= entry.percentile <= 1.0 + + +def test_make_comparison_respects_subset_metrics(patch_metrics, monkeypatch): + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + true_ds = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]), + Split.TEST: _canonical_hf_ds([f"b{i}" for i in range(10)]), + }, + spec=p, + ) + completions = [_ex(answer="aaa", split=Split.TEST)] + + import voice.stylometry.comparison as comparison + + monkeypatch.setattr( + comparison, + "self_wasserstein_distribution", + lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float), + ) + + out = make_comparison(completions, true_ds, metrics=["len"]) + assert out.metrics == ("len",) + assert "len" in out.as_dict() + with pytest.raises(KeyError): + _ = out.get("vowels") + + +def test_make_comparison_uses_observed_split_from_completions( + patch_metrics, monkeypatch +): + p = _pinned(splits=(Split.TRAIN, Split.TEST)) + true_ds = VoiceDataset( + datasets={ + Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]), + Split.TEST: _canonical_hf_ds([f"b{i}" for i in range(10)]), + }, + spec=p, + ) + completions = [_ex(answer="hello", split=Split.TEST)] + + import voice.stylometry.comparison as comparison + + monkeypatch.setattr( + comparison, + "self_wasserstein_distribution", + lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float), + ) + + calls: list[object] = [] + orig_getitem = VoiceDataset.__getitem__ + + def recording_getitem(self, key): + calls.append(key) + return orig_getitem(self, key) + + monkeypatch.setattr(VoiceDataset, "__getitem__", recording_getitem) + + _ = make_comparison(completions, true_ds, metrics=["len"]) + + assert Split.TEST in calls diff --git a/tests/voice/stylometry/test_metrics.py b/tests/voice/stylometry/test_metrics.py new file mode 100644 index 0000000..a678ca2 --- /dev/null +++ b/tests/voice/stylometry/test_metrics.py @@ -0,0 +1,399 @@ +""" +Tests for voice.stylometry.metrics. + +Scope: +- Registry behaviour (decorator + MetricSpec) +- Global invariants shared by all metrics +- Correctness of counting/ratio logic +- Wiring to tokenisers and helper functions +""" + +from __future__ import annotations + +import dataclasses +import math +from collections.abc import Callable + +import pytest + +import voice.stylometry.metrics as metrics + +# ----------------------------------------------------------------------------- +# Helpers +# ----------------------------------------------------------------------------- + + +def _all_metric_items() -> list[tuple[str, metrics.MetricSpec]]: + reg = metrics.get_metrics() + return sorted(reg.items(), key=lambda kv: kv[0]) + + +def _all_metric_names() -> list[str]: + return [name for name, _ in _all_metric_items()] + + +def _ratio_metric_names() -> list[str]: + return [ + "hapax_legomena_ratio", + "dis_legomena_ratio", + "tri_legomena_ratio", + "function_word_ratio", + "type_token_ratio", + "moving_avg_type_token_ratio", + "char_3gram_type_token_ratio", + "char_4gram_type_token_ratio", + "char_5gram_type_token_ratio", + "char_3gram_moving_avg_type_token_ratio", + "char_4gram_moving_avg_type_token_ratio", + "char_5gram_moving_avg_type_token_ratio", + ] + + +def _call_metric(name: str, text: str) -> float: + spec = metrics.get_metrics()[name] + return spec.fn(text) + + +# ----------------------------------------------------------------------------- +# Registry & metadata +# ----------------------------------------------------------------------------- + + +def test_get_metrics_returns_copy(): + m1 = metrics.get_metrics() + m2 = metrics.get_metrics() + assert m1 is not m2 + assert m1.keys() == m2.keys() + + +def test_get_metrics_contains_expected_metric_names(): + expected = { + "avg_word_length", + "std_word_length", + "skew_word_length", + "kurtosis_word_length", + "num_words", + "hapax_legomena_ratio", + "dis_legomena_ratio", + "tri_legomena_ratio", + "function_word_ratio", + "type_token_ratio", + "moving_avg_type_token_ratio", + "char_3gram_type_token_ratio", + "char_4gram_type_token_ratio", + "char_5gram_type_token_ratio", + "char_3gram_moving_avg_type_token_ratio", + "char_4gram_moving_avg_type_token_ratio", + "char_5gram_moving_avg_type_token_ratio", + } + assert expected.issubset(set(_all_metric_names())) + + +def test_metric_decorator_rejects_duplicate_names(monkeypatch): + # Isolate decorator behaviour by swapping the module registry. + monkeypatch.setattr(metrics, "_REGISTRY", {}) + metrics.metric("x")(lambda t: 0.0) + with pytest.raises(ValueError, match=r"Duplicate metric name 'x'"): + metrics.metric("x")(lambda t: 1.0) + + +def test_metric_spec_is_frozen(): + spec = metrics.MetricSpec(fn=lambda t: 0.0, description="x") + assert type(spec).__dataclass_params__.frozen is True + + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(spec, "description", "y") # noqa: B010 + + +def test_metric_spec_repr_contains_function_name_and_description(): + spec = metrics.MetricSpec( + fn=metrics.calculate_avg_word_length, description="Average word length" + ) + r = repr(spec) + assert "MetricSpec(" in r + assert "calculate_avg_word_length" in r + assert "Average word length" in r + + +# ----------------------------------------------------------------------------- +# Global invariants (over all metrics) +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize("metric_name", _all_metric_names()) +def test_all_metrics_return_float(metric_name: str): + v = _call_metric(metric_name, "a bb ccc") + assert isinstance(v, float) + + +@pytest.mark.parametrize("metric_name", _all_metric_names()) +def test_all_metrics_return_zero_on_empty_string(metric_name: str): + assert _call_metric(metric_name, "") == 0.0 + + +@pytest.mark.parametrize("metric_name", _all_metric_names()) +def test_all_metrics_finite_and_deterministic(metric_name: str): + text = "the quick brown fox jumps over the lazy dog" + v1 = _call_metric(metric_name, text) + v2 = _call_metric(metric_name, text) + assert math.isfinite(v1) + assert v1 == v2 + + +def test_all_metrics_do_not_mutate_registry(): + before = metrics.get_metrics() + for name, spec in before.items(): + _ = spec.fn("a bb ccc") + after = metrics.get_metrics() + assert before.keys() == after.keys() + # Ensure metadata didn’t change either + for k in before: + assert before[k] == after[k] + + +# ----------------------------------------------------------------------------- +# Word length moment metrics +# ----------------------------------------------------------------------------- + + +def test_avg_word_length_exact_on_handmade_tokens(monkeypatch): + monkeypatch.setattr(metrics, "word_tokenize", lambda t: ["a", "bb", "ccc"]) + assert metrics.calculate_avg_word_length("ignored") == 2.0 + + +def test_std_word_length_non_negative(monkeypatch): + monkeypatch.setattr(metrics, "word_tokenize", lambda t: ["a", "bb", "ccc"]) + + monkeypatch.setattr( + metrics, "length_central_moment_from_units", lambda units, order: 4.0 + ) + assert metrics.calculate_std_word_length("ignored") == 2.0 + + +def test_skew_and_kurtosis_zero_when_variance_zero(monkeypatch): + monkeypatch.setattr(metrics, "word_tokenize", lambda t: ["a", "bb", "ccc"]) + + def fake_moment(units, order: int) -> float: + if order == 2: + return 0.0 + raise AssertionError( + "Higher order moments shouldn't be requested when variance is zero" + ) + + monkeypatch.setattr( + metrics, "length_central_moment_from_units", fake_moment + ) + + assert metrics.calculate_skew_word_length("ignored") == 0.0 + assert metrics.calculate_kurtosis_word_length("ignored") == 0.0 + + +# ----------------------------------------------------------------------------- +# Count and legomena ratio metrics +# ----------------------------------------------------------------------------- + + +def test_num_words_counts_tokens(monkeypatch): + monkeypatch.setattr( + metrics, "word_tokenize", lambda t: ["a", "b", "c", "d"] + ) + assert metrics.calculate_num_words("ignored") == 4.0 + + +def test_hapax_dis_tri_legomena_ratios_exact_on_handmade_tokens(monkeypatch): + tokens = ["a", "b", "b", "c", "c", "c"] + monkeypatch.setattr(metrics, "word_tokenize", lambda t: tokens) + + assert metrics.calculate_hapax_legomena_ratio("ignored") == pytest.approx( + 1 / 6 + ) + assert metrics.calculate_dis_legomena_ratio("ignored") == pytest.approx( + 1 / 6 + ) + assert metrics.calculate_tri_legomena_ratio("ignored") == pytest.approx( + 1 / 6 + ) + + +def test_function_word_ratio_exact_on_handmade_tokens(monkeypatch): + tokens = ["the", "cat", "and", "dog"] + monkeypatch.setattr(metrics, "word_tokenize", lambda t: tokens) + monkeypatch.setattr(metrics, "FUNCTION_WORDS", {"the", "and"}) + assert metrics.calculate_function_word_ratio("ignored") == 0.5 + + +@pytest.mark.parametrize("metric_name", _ratio_metric_names()) +def test_ratio_metrics_within_unit_interval(metric_name: str): + v = _call_metric( + metric_name, "the quick brown fox jumps over the lazy dog" + ) + assert 0.0 <= v <= 1.0 + + +# ----------------------------------------------------------------------------- +# Word TTR / MATTR metrics +# ----------------------------------------------------------------------------- + + +def test_type_token_ratio_exact_on_handmade_tokens(monkeypatch): + tokens = ["a", "a", "b", "c"] # 3 types / 4 tokens = 0.75 + monkeypatch.setattr(metrics, "word_tokenize", lambda t: tokens) + + monkeypatch.setattr( + metrics, + "ttr_from_units", + lambda units: 0.0 if not units else (len(set(units)) / len(units)), + ) + assert metrics.calculate_type_token_ratio("ignored") == pytest.approx(0.75) + + +def test_moving_avg_type_token_ratio_uses_default_window_and_stride( + monkeypatch, +): + monkeypatch.setattr( + metrics, "word_tokenize", lambda t: ["a", "b", "c", "d"] + ) + + captured = {} + + def spy_mattr(units, *, window_size: int, stride: int) -> float: + captured["window_size"] = window_size + captured["stride"] = stride + return 0.123 + + monkeypatch.setattr(metrics, "mattr_from_units", spy_mattr) + + out = metrics.calculate_moving_avg_type_token_ratio("ignored") + assert out == 0.123 + assert ( + captured["window_size"] == metrics.PARAMETER_DEFAULTS.mattr_window_size + ) + assert captured["stride"] == metrics.PARAMETER_DEFAULTS.mattr_stride + + +def test_moving_avg_type_token_ratio_falls_back_on_short_text(monkeypatch): + monkeypatch.setattr(metrics, "word_tokenize", lambda t: ["a", "a", "b"]) + assert metrics.calculate_moving_avg_type_token_ratio( + "ignored" + ) == pytest.approx(metrics.calculate_type_token_ratio("ignored")) + + +# ----------------------------------------------------------------------------- +# Character n-gram TTR metrics (n = 3,4,5) +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "fn,n", + [ + (metrics.calculate_char_3gram_type_token_ratio, 3), + (metrics.calculate_char_4gram_type_token_ratio, 4), + (metrics.calculate_char_5gram_type_token_ratio, 5), + ], +) +def test_char_ngram_ttr_calls_char_tokenize_with_correct_n( + monkeypatch, fn: Callable[[str], float], n: int +): + seen = {"n": None} + + def spy_char_tokenize(text: str, *, n: int): + seen["n"] = n + return ["x", "y", "z"] + + monkeypatch.setattr(metrics, "char_tokenize", spy_char_tokenize) + monkeypatch.setattr(metrics, "ttr_from_units", lambda units: 0.42) + + assert fn("ignored") == 0.42 + assert seen["n"] == n + + +def test_char_ngram_ttr_zero_on_empty_string(monkeypatch): + monkeypatch.setattr(metrics, "char_tokenize", lambda text, *, n: []) + monkeypatch.setattr(metrics, "ttr_from_units", lambda units: 0.0) + assert metrics.calculate_char_3gram_type_token_ratio("") == 0.0 + assert metrics.calculate_char_4gram_type_token_ratio("") == 0.0 + assert metrics.calculate_char_5gram_type_token_ratio("") == 0.0 + + +# ----------------------------------------------------------------------------- +# Character n-gram MATTR metrics (n = 3,4,5) +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "fn,n", + [ + (metrics.calculate_char_3gram_moving_avg_type_token_ratio, 3), + (metrics.calculate_char_4gram_moving_avg_type_token_ratio, 4), + (metrics.calculate_char_5gram_moving_avg_type_token_ratio, 5), + ], +) +def test_char_ngram_mattr_calls_char_tokenize_with_correct_n( + monkeypatch, fn: Callable[[str], float], n: int +): + seen = {"n": None} + + def spy_char_tokenize(text: str, *, n: int): + seen["n"] = n + return ["x", "y", "z"] + + monkeypatch.setattr(metrics, "char_tokenize", spy_char_tokenize) + monkeypatch.setattr( + metrics, "mattr_from_units", lambda units, *, window_size, stride: 0.33 + ) + + assert fn("ignored") == 0.33 + assert seen["n"] == n + + +def test_char_ngram_mattr_uses_default_window_and_stride(monkeypatch): + monkeypatch.setattr( + metrics, "char_tokenize", lambda text, *, n: ["x", "y", "z"] + ) + + captured = {} + + def spy_mattr(units, *, window_size: int, stride: int) -> float: + captured["window_size"] = window_size + captured["stride"] = stride + return 0.11 + + monkeypatch.setattr(metrics, "mattr_from_units", spy_mattr) + + _ = metrics.calculate_char_3gram_moving_avg_type_token_ratio("ignored") + assert ( + captured["window_size"] + == metrics.PARAMETER_DEFAULTS.mattr_window_size_char + ) + assert captured["stride"] == metrics.PARAMETER_DEFAULTS.mattr_stride_char + + +def test_char_ngram_mattr_falls_back_on_short_text(monkeypatch): + monkeypatch.setattr( + metrics, "char_tokenize", lambda text, *, n: ["x", "y"] + ) + assert metrics.calculate_char_3gram_moving_avg_type_token_ratio( + "ignored" + ) == pytest.approx( + metrics.calculate_char_3gram_type_token_ratio("ignored") + ) + + +# ----------------------------------------------------------------------------- +# Minimal smoke tests +# ----------------------------------------------------------------------------- + + +def test_all_metrics_run_on_simple_sentence_smoke(): + text = "The quick brown fox jumps over the lazy dog." + for name, spec in _all_metric_items(): + v = spec.fn(text) + assert isinstance(v, float) + assert math.isfinite(v) + + +def test_selected_metrics_on_tiny_text_regression(): + text = "a bb ccc" + assert metrics.calculate_num_words(text) == 3.0 + 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