From 6940ccf66ce9947a6ccde7ee41972a7b8532b658 Mon Sep 17 00:00:00 2001 From: Centaur Date: Fri, 25 Sep 2026 23:54:42 +0000 Subject: [PATCH 1/2] feat(dspymator): TypeSafe decision LMs, predict_proba, classifier tags - lm accepts any dspy BaseLM or decision client (dspy.experimental.TypeSafe) - predict/transform unwrap Noul/Choice/Score values - predict_proba + classes_ for a single decision target via dspy record_evidence - tag as classifier so sklearn scorers (roc_auc, neg_log_loss) work - lock dspy 3.4.0; offline tests with a fake decision client --- docs/user-guide/dspymator.md | 31 ++ src/centimators/model_estimators/dspymator.py | 169 ++++++++-- tests/test_dspymator.py | 143 +++++++++ uv.lock | 303 +----------------- 4 files changed, 319 insertions(+), 327 deletions(-) create mode 100644 tests/test_dspymator.py diff --git a/docs/user-guide/dspymator.md b/docs/user-guide/dspymator.md index fdd9b2a..d7d9e34 100644 --- a/docs/user-guide/dspymator.md +++ b/docs/user-guide/dspymator.md @@ -219,6 +219,37 @@ few_shot_classifier.fit( predictions = few_shot_classifier.predict(test_reviews[["review_text"]]) ``` +### Probabilities and Decision Models (TypeSafe) + +With dspy>=3.4, a single output typed as `bool`, `Literal[...]`, or dspy's +experimental `Noul`/`Choice`/`Score` makes DSPyMator a probabilistic classifier: +`predict_proba` and `classes_` work, so sklearn scorers like `roc_auc` and +`neg_log_loss` do too. + +`lm` also accepts TypeSafe's non-generative System One models, which answer +typed decisions with probabilities instead of writing text. Use `dspy.Predict` +and give every output field a `desc`: + +```python +from dspy.experimental import TypeSafe +from sklearn.model_selection import cross_val_score + +class Satire(dspy.Signature): + """Decide whether a news headline is satire.""" + headline: str = dspy.InputField() + is_onion: bool = dspy.OutputField(desc="Is this headline from The Onion?") + +clf = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_onion", + lm=TypeSafe("jev-latest"), # reads TYPESAFE_API_KEY +) +cross_val_score(clf, df.select("headline"), df["is_onion"], cv=5, scoring="roc_auc") +``` + +Optimizers still apply: instructions and demos are sent to the model. GEPA's +`reflection_lm` must be a generative LM. + ### Advanced: Custom Multi-Input Features DSPyMator automatically maps multiple dataframe columns to signature fields: diff --git a/src/centimators/model_estimators/dspymator.py b/src/centimators/model_estimators/dspymator.py index 5908df6..68155f0 100644 --- a/src/centimators/model_estimators/dspymator.py +++ b/src/centimators/model_estimators/dspymator.py @@ -2,19 +2,44 @@ DSPyMator: A scikit-learn compatible wrapper for DSPy modules. """ -from dataclasses import dataclass -from typing import Any import asyncio import warnings +from contextlib import contextmanager +from dataclasses import dataclass +from typing import Any, get_args -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.model_selection import train_test_split +import dspy import narwhals as nw import numpy -import dspy +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.model_selection import train_test_split +from sklearn.utils import ClassifierTags from centimators.narwhals_utils import _ensure_numpy +try: # dspy>=3.4: typed decision outputs with probability evidence + from dspy.adapters.decision import record_evidence + from dspy.adapters.types.decision import Choice, Noul, Score, decision_type +except ImportError: # pragma: no cover - older dspy + Choice = Noul = Score = decision_type = None + + @contextmanager + def record_evidence(): + yield [] + + +def _unwrap(value): + """Return the plain value of a typed decision (Noul/Choice/Score).""" + return ( + value.value + if Noul is not None and isinstance(value, (Noul, Choice, Score)) + else value + ) + + +def _to_python(value): + return value.item() if isinstance(value, numpy.generic) else value + @dataclass(kw_only=True) class DSPyMator(TransformerMixin, BaseEstimator): @@ -45,10 +70,23 @@ class DSPyMator(TransformerMixin, BaseEstimator): returns dataframe with target column(s). For single targets, returns 1D array or single-column dataframe. For multiple targets, returns 2D array or multi-column dataframe. + - `predict_proba(X)`: Class probabilities for a single decision target + (`bool`, `Literal[...]`, or dspy's experimental `Noul`/`Choice`/`Score` + types; requires dspy>=3.4). Columns follow `classes_`. Works with any + backend that returns decision evidence, including generative LMs and + TypeSafe's non-generative System One models. - `transform(X)`: Returns all output fields from the DSPy program (including reasoning, intermediate steps, etc.) as a dataframe in the same backend as the input. Use this to access full program outputs. + Decision models (TypeSafe): + `lm` also accepts `dspy.experimental.TypeSafe(...)`, a non-generative + backend that answers typed decisions with probabilities instead of + writing text. Use `dspy.Predict` (not `ChainOfThought`), give every output + field a `desc`, and keep outputs to decision types. Optimizers still + work: instructions and demos are sent to the model, while GEPA's + `reflection_lm` must remain a generative LM. + Progress Tracking: When `verbose=True`, displays progress bars using tqdm. Requires `tqdm` for sync mode and `tqdm.asyncio` for async mode. Falls back gracefully @@ -117,10 +155,11 @@ class DSPyMator(TransformerMixin, BaseEstimator): feature_names: Column names mapping input data to signature input fields. If None, inferred from dataframe columns or uses signature field names for numpy arrays. Must match the number of input fields in the signature. - lm: Language model - either a string identifier (e.g., "openai/gpt-4") or a - pre-configured `dspy.LM` object. Pass a `dspy.LM` directly when you need + lm: Language model - either a string identifier (e.g., "openai/gpt-4"), a + pre-configured `dspy.LM` object, or a decision client such as + `dspy.experimental.TypeSafe`. Pass an object directly when you need custom configuration like `api_key` or `api_base` for providers like OpenRouter. - When passing an LM object, `temperature` and `max_tokens` are ignored. + When passing an object, `temperature` and `max_tokens` are ignored. Defaults to "openai/gpt-5-nano". temperature: Sampling temperature for the language model. Defaults to 1.0. max_tokens: Maximum tokens in model responses. Defaults to 16000. @@ -170,7 +209,7 @@ class DSPyMator(TransformerMixin, BaseEstimator): program: dspy.Module target_names: str | list[str] feature_names: list[str] | None = None - lm: str | dspy.LM = "openai/gpt-5-nano" + lm: Any = "openai/gpt-5-nano" temperature: float = 1.0 max_tokens: int = 16000 use_async: bool = True @@ -246,12 +285,12 @@ def fit( estimator.fit(X_train, y_train, optimizer=gepa_optimizer, validation_data=0.2) ``` """ - if isinstance(self.lm, dspy.LM): - self.lm_ = self.lm - else: + if isinstance(self.lm, str): self.lm_ = dspy.LM( self.lm, temperature=self.temperature, max_tokens=self.max_tokens ) + else: + self.lm_ = self.lm self.input_fields_ = list(self.signature_.input_fields.keys()) @@ -317,9 +356,27 @@ def fit( if hasattr(optimized_program, "detailed_results"): self.optimizer_results_ = optimized_program.detailed_results + classes = self._decision_classes() + if classes is not None: + self.classes_ = classes + self._is_fitted = True return self + def _decision_classes(self): + """Ordered classes of a single decision target, or None.""" + if decision_type is None or len(self._target_names) != 1: + return None + field = self.signature_.output_fields.get(self._target_names[0]) + kind = decision_type(field) if field is not None else None + if kind is None: + return None + if issubclass(kind, Noul): + return numpy.array([False, True]) + if issubclass(kind, Score): + return numpy.arange(len(kind.criteria())) + return numpy.array(get_args(kind.model_fields["value"].annotation)) + @nw.narwhalify def _convert_to_examples(self, X, y): """Convert X, y data to DSPy Example objects. @@ -337,23 +394,13 @@ def _convert_to_examples(self, X, y): examples = [] # Build input kwargs for each row - if isinstance(X, numpy.ndarray): - input_kwargs_list = [ - {inp: val for inp, val in zip(self.input_fields_, row)} for row in X - ] - else: - input_kwargs_list = [ - { - inp: row[col] - for inp, col in zip(self.input_fields_, self.feature_names) - } - for row in X.iter_rows(named=True) - ] + input_kwargs_list = list(self._iter_input_kwargs(X)) # Add targets and create examples for kwargs, label in zip(input_kwargs_list, y): for i, target_name in enumerate(self._target_names): - kwargs[target_name] = label[i] if len(self._target_names) > 1 else label + value = label[i] if len(self._target_names) > 1 else label + kwargs[target_name] = _to_python(value) examples.append(dspy.Example(**kwargs).with_inputs(*self.input_fields_)) return examples @@ -362,7 +409,9 @@ def _convert_to_examples(self, X, y): def _iter_input_kwargs(self, X): if isinstance(X, numpy.ndarray): for row in X: - yield {inp: val for inp, val in zip(self.input_fields_, row)} + yield { + inp: _to_python(val) for inp, val in zip(self.input_fields_, row) + } else: for row in X.iter_rows(named=True): yield { @@ -370,6 +419,17 @@ def _iter_input_kwargs(self, X): for inp, col in zip(self.input_fields_, self.feature_names) } + def _call_one(self, kwargs): + with record_evidence() as log: + pred = self.program(**kwargs) + return pred, {name: evidence for _, name, evidence in log} + + async def _acall_one(self, kwargs): + # Evidence lives in a ContextVar, so each asyncio task collects only its own row. + with record_evidence() as log: + pred = await self.program.acall(**kwargs) + return pred, {name: evidence for _, name, evidence in log} + def _predict_raw_sync(self, X): """Synchronously predict all samples with optional progress bar.""" input_kwargs = list(self._iter_input_kwargs(X)) @@ -379,7 +439,7 @@ def _predict_raw_sync(self, X): from tqdm import tqdm return [ - self.program(**kwargs) + self._call_one(kwargs) for kwargs in tqdm(input_kwargs, desc="DSPyMator predicting") ] except ImportError: @@ -387,9 +447,9 @@ def _predict_raw_sync(self, X): "tqdm not installed; progress bar unavailable. Install tqdm for progress tracking.", stacklevel=2, ) - return [self.program(**kwargs) for kwargs in input_kwargs] + return [self._call_one(kwargs) for kwargs in input_kwargs] else: - return [self.program(**kwargs) for kwargs in input_kwargs] + return [self._call_one(kwargs) for kwargs in input_kwargs] async def _predict_raw_async(self, X): """Asynchronously predict all samples with bounded concurrency and optional progress bar.""" @@ -398,7 +458,7 @@ async def _predict_raw_async(self, X): async def run_one(kwargs): async with semaphore: - return await self.program.acall(**kwargs) + return await self._acall_one(kwargs) tasks = [run_one(kwargs) for kwargs in input_kwargs] @@ -445,7 +505,7 @@ def _predict_raw(self, X): def predict(self, X): if not hasattr(self, "_is_fitted"): raise ValueError("Classifier not fitted. Call fit() first.") - preds = self._predict_raw(X) + preds = [pred for pred, _ in self._predict_raw(X)] fields = self._target_names if not preds: @@ -463,7 +523,7 @@ def predict(self, X): ) labels = numpy.array( - [[getattr(pred, field) for field in fields] for pred in preds] + [[_unwrap(getattr(pred, field)) for field in fields] for pred in preds] ) predictions = labels.squeeze(axis=1) if len(fields) == 1 else labels @@ -479,6 +539,38 @@ def predict(self, X): cols = {fields[i]: predictions[:, i] for i in range(len(fields))} return nw.from_dict(cols, backend=nw.get_native_namespace(X)) + def predict_proba(self, X): + """Class probabilities for a single decision target, ordered by `classes_`. + + Returns a numpy array of shape (n_samples, n_classes). The backend must + return decision evidence (dspy>=3.4); rows without evidence raise. + """ + if not hasattr(self, "_is_fitted"): + raise ValueError("Classifier not fitted. Call fit() first.") + if not hasattr(self, "classes_"): + raise ValueError( + "predict_proba needs a single target_names field typed as a decision " + "(bool, Literal[...], Noul, Choice, or Score) and dspy>=3.4." + ) + field = self._target_names[0] + rows = [] + for _, evidence in self._predict_raw(X): + answer = evidence.get(field) + if answer is None: + raise ValueError( + f"No decision evidence returned for {field!r}; the backend did not " + "report probabilities." + ) + if "noul" in answer: + p = answer["noul"] + rows.append([1.0 - p, p]) + else: + probs = answer["probabilities"] + rows.append( + [probs.get(c, probs.get(str(c), 0.0)) for c in self.classes_] + ) + return numpy.asarray(rows, dtype=float).reshape(-1, len(self.classes_)) + def _get_output_fields(self): """Get all output fields for transform.""" signature = self._get_signature() @@ -492,11 +584,11 @@ def transform(self, X, y=None): raise ValueError("Classifier not fitted. Call fit() first.") output_fields = self._get_output_fields() - preds = self._predict_raw(X) + preds = [pred for pred, _ in self._predict_raw(X)] # Build a dictionary of columns data = { - field: [getattr(pred, field, None) for pred in preds] + field: [_unwrap(getattr(pred, field, None)) for pred in preds] for field in output_fields } @@ -509,5 +601,14 @@ def fit_transform(self, X, y=None, **kwargs): def get_feature_names_out(self, input_features=None): return self._get_output_fields() + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + # A single decision target makes this a probabilistic classifier, which + # sklearn scorers (roc_auc, neg_log_loss, ...) require for predict_proba. + if self._decision_classes() is not None: + tags.estimator_type = "classifier" + tags.classifier_tags = ClassifierTags() + return tags + def __sklearn_is_fitted__(self): return getattr(self, "_is_fitted", False) diff --git a/tests/test_dspymator.py b/tests/test_dspymator.py new file mode 100644 index 0000000..607d885 --- /dev/null +++ b/tests/test_dspymator.py @@ -0,0 +1,143 @@ +from typing import Literal + +import numpy as np +import pytest + +dspy = pytest.importorskip("dspy") +pytest.importorskip("dspy.adapters.decision", reason="decision outputs need dspy>=3.4") + +import polars as pl +from sklearn.model_selection import StratifiedKFold, cross_val_score +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import FunctionTransformer + +from centimators.model_estimators import DSPyMator + + +class FakeDecisionLM: + """Offline stand-in for dspy.experimental.TypeSafe: scores text by keyword.""" + + supports_decision_requests = True + + def __init__(self): + self.calls = 0 + + def __call__(self, state, questions): + self.calls += 1 + text = str(state["inputs"]).lower() + hit = "onion" in text + answers = {} + for name, question in questions.items(): + if question["type"] == "noul": + answers[name] = {"noul": 0.9 if hit else 0.2} + else: + labels = list(question["criteria"]) + probs = {label: 0.0 for label in labels} + probs[labels[0] if hit else labels[-1]] = 0.7 + probs[labels[-1] if hit else labels[0]] += 0.3 + answers[name] = { + "choice": max(probs, key=probs.get), + "confidence": 0.4, + "probabilities": probs, + } + return answers + + async def acall(self, state, questions): + return self(state, questions) + + +class Satire(dspy.Signature): + """Decide whether a headline is satire.""" + + headline: str = dspy.InputField() + is_satire: bool = dspy.OutputField(desc="Is this headline satire?") + + +class Topic(dspy.Signature): + """Pick the section.""" + + headline: str = dspy.InputField() + section: Literal["satire", "politics", "sports"] = dspy.OutputField( + desc="Which section?" + ) + + +def _data(n=12): + headlines = [ + f"onion reports man {i} wins" if i % 2 else f"senate votes {i}" + for i in range(n) + ] + X = pl.DataFrame({"headline": headlines}) + y = np.array([i % 2 == 1 for i in range(n)]) + return X, y + + +@pytest.mark.parametrize("use_async", [True, False]) +def test_decision_lm_predict_and_proba(use_async): + X, y = _data() + lm = FakeDecisionLM() + est = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_satire", + lm=lm, + use_async=use_async, + verbose=False, + ).fit(X, y) + + assert est.lm_ is lm + assert list(est.classes_) == [False, True] + + pred = est.predict(X) + assert isinstance(pred, pl.DataFrame) + assert pred["is_satire"].to_list() == y.tolist() + + proba = est.predict_proba(X) + assert proba.shape == (len(X), 2) + np.testing.assert_allclose(proba.sum(axis=1), 1.0) + np.testing.assert_allclose(proba[:, 1], np.where(y, 0.9, 0.2)) + + +def test_choice_proba_follows_classes(): + X, y = _data(4) + est = DSPyMator( + program=dspy.Predict(Topic), + target_names="section", + lm=FakeDecisionLM(), + verbose=False, + ).fit(X, None) + + assert list(est.classes_) == ["satire", "politics", "sports"] + proba = est.predict_proba(X) + assert proba.shape == (4, 3) + assert est.predict(X)["section"].to_list() == [ + "satire" if s else "sports" for s in y + ] + np.testing.assert_allclose(proba[1], [0.7, 0.0, 0.3]) + + +def test_sklearn_scorer_and_pipeline_with_decision_lm(): + X, y = _data(12) + est = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_satire", + lm=FakeDecisionLM(), + verbose=False, + ) + pipe = make_pipeline(FunctionTransformer(lambda df: df), est) + scores = cross_val_score( + pipe, X, y, cv=StratifiedKFold(3), scoring="roc_auc", error_score="raise" + ) + np.testing.assert_allclose(scores, 1.0) + + +def test_predict_proba_requires_decision_target(): + X, _ = _data(2) + est = DSPyMator( + program=dspy.Predict("headline -> summary"), + target_names="summary", + lm=FakeDecisionLM(), + verbose=False, + ).fit(X, None) + assert not hasattr(est, "classes_") + with pytest.raises(ValueError, match="predict_proba needs"): + est.predict_proba(X) diff --git a/uv.lock b/uv.lock index 6ea235d..0dcdd2f 100644 --- a/uv.lock +++ b/uv.lock @@ 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deletions(-) diff --git a/docs/user-guide/dspymator.md b/docs/user-guide/dspymator.md index d7d9e34..3df6976 100644 --- a/docs/user-guide/dspymator.md +++ b/docs/user-guide/dspymator.md @@ -192,6 +192,32 @@ optimized_classifier.fit( predictions = optimized_classifier.predict(test_reviews[["review_text"]]) ``` +#### Tuning optimizer settings with GridSearchCV + +Wrap the optimizer in `DSPyOptimizer` and pass it to the constructor. Each keyword becomes a nested hyperparameter, a fresh optimizer is built on every fit, and each CV fold does its own validation split: + +```python +from centimators.model_estimators import DSPyMator, DSPyOptimizer +from sklearn.model_selection import GridSearchCV, StratifiedKFold + +est = DSPyMator( + program=dspy.Predict(Sentiment), + target_names="sentiment", + lm=lm, + optimizer=DSPyOptimizer(dspy.GEPA, metric=metric, reflection_lm=reflection_lm, + max_metric_calls=400), + validation_data=0.5, +) +search = GridSearchCV( + est, + {"optimizer__reflection_minibatch_size": [3, 8], "optimizer__max_metric_calls": [400, 1200]}, + cv=StratifiedKFold(3), + scoring="f1_macro", +).fit(X, y) +``` + +Inside a `Pipeline` the keys gain the step prefix (`dspymator__optimizer__...`). Nothing needs metadata routing: optimizer settings are hyperparameters, not per-row metadata. Prefer a `validation_data` fraction over a fixed `(X_val, y_val)` inside CV, so each fold validates on its own rows. + ### Few-Shot Learning with Bootstrap For few-shot learning, use `BootstrapFewShot` to automatically select good demonstrations: @@ -396,6 +422,8 @@ llm_pipeline = make_pipeline( | `use_async` | `bool` | `True` | Use async execution for batch predictions | | `max_concurrent` | `int` | `50` | Maximum concurrent requests in async mode | | `verbose` | `bool` | `True` | Show progress bars during prediction | +| `optimizer` | `DSPyOptimizer \| dspy optimizer \| None` | `None` | Optimizer run on every fit; wrap in `DSPyOptimizer` to grid-search its settings | +| `validation_data` | `tuple \| float \| None` | `None` | Default validation data for the optimizer (see below) | ### fit() Parameters @@ -403,5 +431,5 @@ llm_pipeline = make_pipeline( |-----------|------|---------|-------------| | `X` | DataFrame/array | *required* | Training features | | `y` | Series/array | *required* | Target values (can be `None` for unsupervised) | -| `optimizer` | `dspy.Optimizer \| None` | `None` | DSPy optimizer instance (e.g., `dspy.GEPA`, `dspy.BootstrapFewShot`) | +| `optimizer` | `dspy.Optimizer \| None` | `None` | Overrides the constructor `optimizer` for this fit | | `validation_data` | `tuple \| float \| None` | `None` | Validation data as `(X_val, y_val)`, a float for train split fraction, or `None` | \ No newline at end of file diff --git a/src/centimators/model_estimators/__init__.py b/src/centimators/model_estimators/__init__.py index da9d670..e48f877 100644 --- a/src/centimators/model_estimators/__init__.py +++ b/src/centimators/model_estimators/__init__.py @@ -19,6 +19,7 @@ "TemperatureAnnealing", # DSPy estimator "DSPyMator", + "DSPyOptimizer", # Meta-estimator "KerasCortex", ] @@ -35,6 +36,7 @@ "TemperatureAnnealing": "centimators.model_estimators.keras_estimators.tree", # DSPy estimator "DSPyMator": "centimators.model_estimators.dspymator", + "DSPyOptimizer": "centimators.model_estimators.dspymator", # Meta-estimator "KerasCortex": "centimators.model_estimators.keras_cortex", } diff --git a/src/centimators/model_estimators/dspymator.py b/src/centimators/model_estimators/dspymator.py index 68155f0..947ab00 100644 --- a/src/centimators/model_estimators/dspymator.py +++ b/src/centimators/model_estimators/dspymator.py @@ -3,6 +3,7 @@ """ import asyncio +import copy import warnings from contextlib import contextmanager from dataclasses import dataclass @@ -11,7 +12,7 @@ import dspy import narwhals as nw import numpy -from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.base import BaseEstimator, TransformerMixin, clone from sklearn.model_selection import train_test_split from sklearn.utils import ClassifierTags @@ -41,6 +42,41 @@ def _to_python(value): return value.item() if isinstance(value, numpy.generic) else value +class DSPyOptimizer: + """A DSPy optimizer as an sklearn hyperparameter: the class plus its keyword arguments. + + DSPyMator builds a fresh optimizer from it on every fit, and each keyword is a + nested parameter, so `GridSearchCV` can tune optimizer settings directly: + + ```python + est = DSPyMator(program=..., target_names="label", lm=lm, + optimizer=DSPyOptimizer(dspy.GEPA, metric=m, reflection_lm=r, + max_metric_calls=400), + validation_data=0.5) + GridSearchCV(est, {"optimizer__reflection_minibatch_size": [3, 8]}).fit(X, y) + ``` + """ + + def __init__(self, optimizer_cls, **kwargs): + self.optimizer_cls = optimizer_cls + self.kwargs = kwargs + + def get_params(self, deep=True): + return {"optimizer_cls": self.optimizer_cls, **self.kwargs} + + def set_params(self, **params): + self.optimizer_cls = params.pop("optimizer_cls", self.optimizer_cls) + self.kwargs.update(params) + return self + + def build(self): + return self.optimizer_cls(**self.kwargs) + + def __repr__(self): + args = ", ".join(f"{k}={v!r}" for k, v in self.kwargs.items()) + return f"DSPyOptimizer({self.optimizer_cls.__name__}, {args})" + + @dataclass(kw_only=True) class DSPyMator(TransformerMixin, BaseEstimator): """DSPyMator is a scikit-learn compatible wrapper for DSPy modules. @@ -215,6 +251,8 @@ class DSPyMator(TransformerMixin, BaseEstimator): use_async: bool = True max_concurrent: int = 50 verbose: bool = True + optimizer: Any = None + validation_data: Any = None def _get_signature(self): """Extract signature from the DSPy program. @@ -260,7 +298,10 @@ def fit( y: Target values (can be None for unsupervised tasks). optimizer: Optional DSPy optimizer instance (e.g., dspy.GEPA, dspy.BootstrapFewShot, dspy.MIPROv2). When provided, enables prompt optimization or finetuning during fit. - validation_data: Validation data for optimizers that require it. + Overrides the `optimizer` constructor parameter, which is the form + `GridSearchCV` and `clone` can see. + validation_data: Validation data for optimizers that require it; overrides + the `validation_data` constructor parameter. - If tuple: Use as (X_val, y_val) directly. - If float (0-1): Fraction of training data to use for validation. - If None: No validation set (for optimizers that only need trainset). @@ -306,6 +347,13 @@ def fit( f"number of input_fields ({len(self.input_fields_)})" ) + if optimizer is None: + optimizer = self.optimizer + if validation_data is None: + validation_data = self.validation_data + if isinstance(optimizer, DSPyOptimizer): + optimizer = optimizer.build() + # Optimization if requested if optimizer is not None: # Handle validation_data parameter @@ -612,3 +660,13 @@ def __sklearn_tags__(self): def __sklearn_is_fitted__(self): return getattr(self, "_is_fitted", False) + + def __sklearn_clone__(self): + # A raw DSPy optimizer exposes get_params() without `deep`, so sklearn's + # clone would treat it as an estimator and fail; copy it whole. + raw = lambda name, v: name == "optimizer" and not isinstance(v, DSPyOptimizer) # noqa: E731 + params = { + name: copy.deepcopy(value) if raw(name, value) else clone(value, safe=False) + for name, value in self.get_params(deep=False).items() + } + return type(self)(**params) diff --git a/tests/test_dspymator.py b/tests/test_dspymator.py index 607d885..82192d7 100644 --- a/tests/test_dspymator.py +++ b/tests/test_dspymator.py @@ -1,17 +1,17 @@ from typing import Literal import numpy as np +import polars as pl import pytest +import sklearn +from sklearn.model_selection import GridSearchCV, StratifiedKFold, cross_val_score +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import FunctionTransformer dspy = pytest.importorskip("dspy") pytest.importorskip("dspy.adapters.decision", reason="decision outputs need dspy>=3.4") -import polars as pl -from sklearn.model_selection import StratifiedKFold, cross_val_score -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import FunctionTransformer - -from centimators.model_estimators import DSPyMator +from centimators.model_estimators import DSPyMator, DSPyOptimizer # noqa: E402 class FakeDecisionLM: @@ -130,6 +130,64 @@ def test_sklearn_scorer_and_pipeline_with_decision_lm(): np.testing.assert_allclose(scores, 1.0) +def test_optimizer_settings_are_searchable_hyperparameters(): + X, y = _data(12) + est = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_satire", + lm=FakeDecisionLM(), + verbose=False, + optimizer=DSPyOptimizer(dspy.LabeledFewShot, k=1), + ) + search = GridSearchCV( + est, + {"optimizer__k": [1, 4]}, + cv=StratifiedKFold(3), + scoring="roc_auc", + error_score="raise", + ).fit(X, y) + + assert search.best_params_["optimizer__k"] in (1, 4) + assert ( + len(search.best_estimator_.program.demos) == search.best_params_["optimizer__k"] + ) + assert est.program.demos == [] + assert est.optimizer.kwargs == {"k": 1} + + +def test_search_composes_with_metadata_routing(): + X, y = _data(12) + est = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_satire", + lm=FakeDecisionLM(), + verbose=False, + optimizer=DSPyOptimizer(dspy.LabeledFewShot, k=1), + ) + pipe = make_pipeline(FunctionTransformer(lambda df: df), est) + with sklearn.config_context(enable_metadata_routing=True): + search = GridSearchCV( + pipe, + {"dspymator__optimizer__k": [1, 2]}, + cv=StratifiedKFold(3), + scoring="neg_log_loss", + error_score="raise", + ).fit(X, y) + assert search.best_params_["dspymator__optimizer__k"] in (1, 2) + + +def test_fit_optimizer_argument_overrides_constructor(): + X, y = _data(6) + est = DSPyMator( + program=dspy.Predict(Satire), + target_names="is_satire", + lm=FakeDecisionLM(), + verbose=False, + optimizer=dspy.LabeledFewShot(k=1), + ).fit(X, y, optimizer=dspy.LabeledFewShot(k=3)) + assert len(est.program.demos) == 3 + + def test_predict_proba_requires_decision_target(): X, _ = _data(2) est = DSPyMator(