26152825991

Committed 20 May 2026 09:08AM UTC coverage: 80.863% (-0.04%) from 80.903%

Build # 26152825991

Build Type

Pull #1966

github

Committed by

web-flow

Commit Message

Merge 914761465 into 37e11e0f6

Pull Request Pull Request #1966: fix: CI compatibility fixes (HF_TOKEN, arena-hard migration, datasets 4.8.5)

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src/unitxt/api.py

import hashlib
import inspect
import json
from datetime import datetime
from typing import Any, Dict, List, Optional, Union

from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
from datasets.exceptions import DatasetGenerationError

from .artifact import fetch_artifact
from .benchmark import Benchmark
from .card import TaskCard
from .dataclass import to_dict
from .dataset_utils import get_dataset_artifact
from .error_utils import UnitxtError
from .inference import (
    InferenceEngine,
    LogProbInferenceEngine,
    OptionSelectingByLogProbsInferenceEngine,
)
from .loaders import LoadFromDictionary
from .logging_utils import get_logger
from .metric_utils import EvaluationResults, _compute, _inference_post_process
from .operator import SourceOperator
from .schema import loads_batch
from .settings_utils import get_constants, get_settings
from .standard import DatasetRecipe
from .task import Task
from .utils import lru_cache_decorator

logger = get_logger()
constants = get_constants()
settings = get_settings()


def short_hex_hash(value, length=8):
    h = hashlib.sha256(value.encode()).hexdigest()  # Full 64-character hex
    return h[:length]


def _get_recipe_from_query(
    dataset_query: str, overwrite_kwargs: Optional[Dict[str, Any]] = None
) -> DatasetRecipe:
    try:
        dataset_stream, _ = fetch_artifact(
            dataset_query, overwrite_kwargs=overwrite_kwargs
        )
    except:
        dataset_stream = get_dataset_artifact(
            dataset_query, overwrite_kwargs=overwrite_kwargs
        )
    return dataset_stream


def _get_recipe_from_dict(dataset_params: Dict[str, Any]) -> DatasetRecipe:
    recipe_attributes = list(DatasetRecipe.__dict__["__fields__"].keys())
    for param in dataset_params.keys():
        assert param in recipe_attributes, (
            f"The parameter '{param}' is not an attribute of the 'DatasetRecipe' class. "
            f"Please check if the name is correct. The available attributes are: '{recipe_attributes}'."
        )
    return DatasetRecipe(**dataset_params)


def _verify_dataset_args(dataset_query: Optional[str] = None, dataset_args=None):
    if dataset_query and dataset_args:
        raise ValueError(
            "Cannot provide 'dataset_query' and key-worded arguments at the same time. "
            "If you want to load dataset from a card in local catalog, use query only. "
            "Otherwise, use key-worded arguments only to specify properties of dataset."
        )

    if dataset_query:
        if not isinstance(dataset_query, str):
            raise ValueError(
                f"If specified, 'dataset_query' must be a string, however, "
                f"'{dataset_query}' was provided instead, which is of type "
                f"'{type(dataset_query)}'."
            )

    if not dataset_query and not dataset_args:
        raise ValueError(
            "Either 'dataset_query' or key-worded arguments must be provided."
        )


def load_recipe(dataset_query: Optional[str] = None, **kwargs) -> DatasetRecipe:
    if isinstance(dataset_query, (DatasetRecipe, Benchmark)):
        return dataset_query

    if dataset_query:
        recipe = _get_recipe_from_query(dataset_query, kwargs)

    elif kwargs:
        recipe = _get_recipe_from_dict(kwargs)

    else:
        raise UnitxtError(
            "Specify either dataset recipe string artifact name or recipe args."
        )

    return recipe


def create_dataset(
    task: Union[str, Task],
    test_set: List[Dict[Any, Any]],
    train_set: Optional[List[Dict[Any, Any]]] = None,
    validation_set: Optional[List[Dict[Any, Any]]] = None,
    split: Optional[str] = None,
    data_classification_policy: Optional[List[str]] = None,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Creates dataset from input data based on a specific task.

    Args:
        task:  The name of the task from the Unitxt Catalog (https://www.unitxt.ai/en/latest/catalog/catalog.tasks.__dir__.html)
        test_set : required list of instances
        train_set : optional train_set
        validation_set: optional validation set
        split: optional one split to choose
        data_classification_policy: data_classification_policy
        **kwargs: Arguments used to load dataset from provided datasets (see load_dataset())

    Returns:
        DatasetDict

    Example:
        template = Template(...)
        dataset = create_dataset(task="tasks.qa.open", template=template, format="formats.chatapi")
    """
    data = {"test": test_set}
    if train_set is not None:
        data["train"] = train_set
    if validation_set is not None:
        data["validation"] = validation_set
    task, _ = fetch_artifact(task)

    if "template" not in kwargs and task.default_template is None:
        raise Exception(
            f"No 'template' was passed to the create_dataset() and the given task ('{task.__id__}') has no 'default_template' field."
        )

    card = TaskCard(
        loader=LoadFromDictionary(
            data=data, data_classification_policy=data_classification_policy
        ),
        task=task,
    )
    return load_dataset(card=card, split=split, **kwargs)


def object_to_str_without_addresses(obj):
    """Generates a string representation of a Python object while removing memory address references.

    This function is useful for creating consistent and comparable string representations of objects
    that would otherwise include memory addresses (e.g., `<object_name at 0x123abc>`), which can vary
    between executions. By stripping the memory address, the function ensures that the representation
    is stable and independent of the object's location in memory.

    Args:
        obj: Any Python object to be converted to a string representation.

    Returns:
        str: A string representation of the object with memory addresses removed if present.

    Example:
        ```python
        class MyClass:
            pass

        obj = MyClass()
        print(str(obj))  # "<__main__.MyClass object at 0x7f8b9d4d6e20>"
        print(to_str_without_addresses(obj))  # "<__main__.MyClass object>"
        ```
    """
    obj_str = str(obj)
    if " at 0x" in obj_str:
        obj_str = obj_str.split(" at 0x")[0] + ">"
    return obj_str


def _source_to_dataset(
    source: SourceOperator,
    split=None,
    use_cache=False,
    streaming=False,
):
    from .dataset import Dataset as UnitxtDataset

    # Generate a unique signature for the source
    source_signature = json.dumps(
        to_dict(source, object_to_str_without_addresses), sort_keys=True
    )
    config_name = "recipe-" + short_hex_hash(source_signature)
    # Obtain data stream from the source
    stream = source()

    try:
        ds_builder = UnitxtDataset(
            dataset_name="unitxt",
            config_name=config_name,  # Dictate the cache name
            version=constants.version,
        )
        if split is not None:
            stream = {split: stream[split]}
        ds_builder._generators = stream

        try:
            ds_builder.download_and_prepare(
                verification_mode="no_checks",
                download_mode=None if use_cache else "force_redownload",
            )
        except DatasetGenerationError as e:
            if e.__cause__:
                raise e.__cause__ from None
            if e.__context__:
                raise e.__context__ from None
            raise

        if streaming:
            return ds_builder.as_streaming_dataset(split=split)

        return ds_builder.as_dataset(split=split)

    except DatasetGenerationError as e:
        raise e.__cause__


def load_dataset(
    dataset_query: Optional[str] = None,
    split: Optional[str] = None,
    streaming: bool = False,
    use_cache: Optional[bool] = None,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Loads dataset.

    If the 'dataset_query' argument is provided, then dataset is loaded from a card
    in local catalog based on parameters specified in the query.

    Alternatively, dataset is loaded from a provided card based on explicitly
    given parameters.

    If both are given, then the textual recipe is loaded with the key word args overriding the textual recipe args.

    Args:
        dataset_query (str, optional):
            A string query which specifies a dataset to load from
            local catalog or name of specific recipe or benchmark in the catalog. For
            example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
        streaming (bool, False):
            When True yields the data as a stream.
            This is useful when loading very large datasets.
            Loading datasets as streams avoid loading all the data to memory, but requires the dataset's loader to support streaming.
        split (str, optional):
            The split of the data to load
        use_cache (bool, optional):
            If set to True, the returned Huggingface dataset is cached on local disk such that if the same dataset is loaded again, it will be loaded from local disk, resulting in faster runs.
            If set to False, the returned dataset is not cached.
            If set to None, the value of this parameter will be determined by setting.dataset_cache_default (default is False).
            Note that if caching is enabled and the dataset card definition is changed, the old version in the cache may be returned.
            Enable caching only if you are sure you are working with fixed Unitxt datasets and definitions (e.g. running using predefined datasets from the Unitxt catalog).
        **kwargs:
            Arguments used to load dataset from provided card, which is not present in local catalog.

    Returns:
        DatasetDict

    :Example:

        .. code-block:: python

            dataset = load_dataset(
                dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
            )  # card and template must be present in local catalog

            # or built programmatically
            card = TaskCard(...)
            template = Template(...)
            loader_limit = 10
            dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)

    """
    recipe = load_recipe(dataset_query, **kwargs)

    if use_cache is None:
        use_cache = settings.dataset_cache_default

    dataset = _source_to_dataset(
        source=recipe, split=split, use_cache=use_cache, streaming=streaming
    )

    frame = inspect.currentframe()
    args, _, _, values = inspect.getargvalues(frame)
    all_kwargs = {key: values[key] for key in args if key != "kwargs"}
    all_kwargs.update(kwargs)
    metadata = fill_metadata(**all_kwargs)
    if isinstance(dataset, dict):
        for ds in dataset.values():
            ds.info.description = metadata.copy()
    else:
        dataset.info.description = metadata
    return dataset


def fill_metadata(**kwargs):
    metadata = kwargs.copy()
    metadata["unitxt_version"] = get_constants().version
    metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
    return metadata


def evaluate(
    predictions: Optional[List[str]] = None,
    dataset: Union[Dataset, IterableDataset] = None,
    data=None,
    calc_confidence_intervals: bool = True,
) -> EvaluationResults:
    if dataset is None and data is None:
        raise UnitxtError(message="Specify 'dataset' in evaluate")
    if data is not None:
        dataset = data  # for backward compatibility
    evaluation_result = _compute(
        predictions=predictions,
        references=dataset,
        calc_confidence_intervals=calc_confidence_intervals,
    )
    if hasattr(dataset, "info") and hasattr(dataset.info, "description"):
        evaluation_result.metadata["dataset"] = dataset.info.description
    if hasattr(predictions, "metadata"):
        evaluation_result.metadata["predictions"] = predictions.metadata
    evaluation_result.metadata["creation_time"] = datetime.now().strftime(
        "%Y-%m-%d %H:%M:%S.%f"
    )[:-3]
    return evaluation_result


def post_process(predictions, data) -> List[Dict[str, Any]]:
    return _inference_post_process(predictions=predictions, references=data)


@lru_cache_decorator(max_size=128)
def _get_recipe_with_cache(dataset_query: Optional[str] = None, **kwargs):
    return load_recipe(dataset_query, **kwargs)


def produce(
    instance_or_instances, dataset_query: Optional[str] = None, **kwargs
) -> Union[Dataset, Dict[str, Any]]:
    is_list = isinstance(instance_or_instances, list)
    if not is_list:
        instance_or_instances = [instance_or_instances]
    dataset_recipe = _get_recipe_with_cache(dataset_query, **kwargs)
    result = dataset_recipe.produce(instance_or_instances)
    if not is_list:
        return result[0]
    return Dataset.from_list(result).with_transform(loads_batch)


def infer(
    instance_or_instances,
    engine: InferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    return_log_probs: bool = False,
    return_meta_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    if return_log_probs:
        if not isinstance(engine, LogProbInferenceEngine):
            raise NotImplementedError(
                f"Error in infer: return_log_probs set to True but supplied engine "
                f"{engine.__class__.__name__} does not support logprobs."
            )
        infer_outputs = engine.infer_log_probs(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
        raw_predictions = [
            json.dumps(raw_prediction) for raw_prediction in raw_predictions
        ]
    else:
        infer_outputs = engine.infer(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
    predictions = post_process(raw_predictions, dataset)
    if return_data:
        if return_meta_data:
            infer_output_list = [
                infer_output.__dict__ for infer_output in infer_outputs
            ]
            for infer_output in infer_output_list:
                del infer_output["prediction"]
            dataset = dataset.add_column("infer_meta_data", infer_output_list)
        dataset = dataset.add_column("prediction", predictions)
        return dataset.add_column("raw_prediction", raw_predictions)
    return predictions


def select(
    instance_or_instances,
    engine: OptionSelectingByLogProbsInferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    predictions = engine.select(dataset)
    # predictions = post_process(raw_predictions, dataset)
    if return_data:
        return dataset.add_column("prediction", predictions)
    return predictions

1	import hashlib	1✔
2	import inspect	1✔
3	import json	1✔
4	from datetime import datetime	1✔
5	from typing import Any, Dict, List, Optional, Union	1✔
6
7	from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict	1✔
8	from datasets.exceptions import DatasetGenerationError	1✔
9
10	from .artifact import fetch_artifact	1✔
11	from .benchmark import Benchmark	1✔
12	from .card import TaskCard	1✔
13	from .dataclass import to_dict	1✔
14	from .dataset_utils import get_dataset_artifact	1✔
15	from .error_utils import UnitxtError	1✔
16	from .inference import (	1✔
17	InferenceEngine,
18	LogProbInferenceEngine,
19	OptionSelectingByLogProbsInferenceEngine,
20	)
21	from .loaders import LoadFromDictionary	1✔
22	from .logging_utils import get_logger	1✔
23	from .metric_utils import EvaluationResults, _compute, _inference_post_process	1✔
24	from .operator import SourceOperator	1✔
25	from .schema import loads_batch	1✔
26	from .settings_utils import get_constants, get_settings	1✔
27	from .standard import DatasetRecipe	1✔
28	from .task import Task	1✔
29	from .utils import lru_cache_decorator	1✔
30
31	logger = get_logger()
32	constants = get_constants()	1✔
33	settings = get_settings()	1✔
34
35
36	def short_hex_hash(value, length=8):	1✔
37	h = hashlib.sha256(value.encode()).hexdigest() # Full 64-character hex	1✔
38	return h[:length]	1✔
39
40
41	def _get_recipe_from_query(	1✔
42	dataset_query: str, overwrite_kwargs: Optional[Dict[str, Any]] = None
43	) -> DatasetRecipe:
44	try:	1✔
45	dataset_stream, _ = fetch_artifact(	1✔
46	dataset_query, overwrite_kwargs=overwrite_kwargs
47	)
48	except:	1✔
49	dataset_stream = get_dataset_artifact(	1✔
50	dataset_query, overwrite_kwargs=overwrite_kwargs
51	)
52	return dataset_stream	1✔
53
54
55	def _get_recipe_from_dict(dataset_params: Dict[str, Any]) -> DatasetRecipe:	1✔
56	recipe_attributes = list(DatasetRecipe.__dict__["__fields__"].keys())	1✔
57	for param in dataset_params.keys():	1✔
58	assert param in recipe_attributes, (	1✔
59	f"The parameter '{param}' is not an attribute of the 'DatasetRecipe' class. "
60	f"Please check if the name is correct. The available attributes are: '{recipe_attributes}'."
61	)
62	return DatasetRecipe(**dataset_params)	1✔
63
64
65	def _verify_dataset_args(dataset_query: Optional[str] = None, dataset_args=None):	1✔
66	if dataset_query and dataset_args:	×
67	raise ValueError(
68	"Cannot provide 'dataset_query' and key-worded arguments at the same time. "
69	"If you want to load dataset from a card in local catalog, use query only. "
70	"Otherwise, use key-worded arguments only to specify properties of dataset."
71	)
72
73	if dataset_query:	×
74	if not isinstance(dataset_query, str):	×
75	raise ValueError(
76	f"If specified, 'dataset_query' must be a string, however, "
77	f"'{dataset_query}' was provided instead, which is of type "
78	f"'{type(dataset_query)}'."
79	)
80
81	if not dataset_query and not dataset_args:	×
82	raise ValueError(
83	"Either 'dataset_query' or key-worded arguments must be provided."
84	)
85
86
87	def load_recipe(dataset_query: Optional[str] = None, **kwargs) -> DatasetRecipe:	1✔
88	if isinstance(dataset_query, (DatasetRecipe, Benchmark)):	1✔
89	return dataset_query	×
90
91	if dataset_query:	1✔
92	recipe = _get_recipe_from_query(dataset_query, kwargs)	1✔
93
94	elif kwargs:	1✔
95	recipe = _get_recipe_from_dict(kwargs)	1✔
96
97	else:
98	raise UnitxtError(	×
99	"Specify either dataset recipe string artifact name or recipe args."
100	)
101
102	return recipe	1✔
103
104
105	def create_dataset(	1✔
106	task: Union[str, Task],
107	test_set: List[Dict[Any, Any]],
108	train_set: Optional[List[Dict[Any, Any]]] = None,
109	validation_set: Optional[List[Dict[Any, Any]]] = None,
110	split: Optional[str] = None,
111	data_classification_policy: Optional[List[str]] = None,
112	**kwargs,
113	) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
114	"""Creates dataset from input data based on a specific task.
115
116	Args:
117	task: The name of the task from the Unitxt Catalog (https://www.unitxt.ai/en/latest/catalog/catalog.tasks.__dir__.html)
118	test_set : required list of instances
119	train_set : optional train_set
120	validation_set: optional validation set
121	split: optional one split to choose
122	data_classification_policy: data_classification_policy
123	**kwargs: Arguments used to load dataset from provided datasets (see load_dataset())
124
125	Returns:
126	DatasetDict
127
128	Example:
129	template = Template(...)
130	dataset = create_dataset(task="tasks.qa.open", template=template, format="formats.chatapi")
131	"""
132	data = {"test": test_set}	1✔
133	if train_set is not None:	1✔
134	data["train"] = train_set	1✔
135	if validation_set is not None:	1✔
136	data["validation"] = validation_set	1✔
137	task, _ = fetch_artifact(task)	1✔
138
139	if "template" not in kwargs and task.default_template is None:	1✔
140	raise Exception(
141	f"No 'template' was passed to the create_dataset() and the given task ('{task.__id__}') has no 'default_template' field."
142	)
143
144	card = TaskCard(	1✔
145	loader=LoadFromDictionary(
146	data=data, data_classification_policy=data_classification_policy
147	),
148	task=task,
149	)
150	return load_dataset(card=card, split=split, **kwargs)	1✔
151
152
153	def object_to_str_without_addresses(obj):	1✔
154	"""Generates a string representation of a Python object while removing memory address references.
155
156	This function is useful for creating consistent and comparable string representations of objects
157	that would otherwise include memory addresses (e.g., `<object_name at 0x123abc>`), which can vary
158	between executions. By stripping the memory address, the function ensures that the representation
159	is stable and independent of the object's location in memory.
160
161	Args:
162	obj: Any Python object to be converted to a string representation.
163
164	Returns:
165	str: A string representation of the object with memory addresses removed if present.
166
167	Example:
168	```python
169	class MyClass:
170	pass
171
172	obj = MyClass()
173	print(str(obj)) # "<__main__.MyClass object at 0x7f8b9d4d6e20>"
174	print(to_str_without_addresses(obj)) # "<__main__.MyClass object>"
175	```
176	"""
177	obj_str = str(obj)	1✔
178	if " at 0x" in obj_str:	1✔
179	obj_str = obj_str.split(" at 0x")[0] + ">"	1✔
180	return obj_str	1✔
181
182
183	def _source_to_dataset(	1✔
184	source: SourceOperator,
185	split=None,
186	use_cache=False,
187	streaming=False,
188	):
189	from .dataset import Dataset as UnitxtDataset	1✔
190
191	# Generate a unique signature for the source
192	source_signature = json.dumps(	1✔
193	to_dict(source, object_to_str_without_addresses), sort_keys=True
194	)
195	config_name = "recipe-" + short_hex_hash(source_signature)	1✔
196	# Obtain data stream from the source
197	stream = source()	1✔
198
199	try:	1✔
200	ds_builder = UnitxtDataset(	1✔
201	dataset_name="unitxt",
202	config_name=config_name, # Dictate the cache name
203	version=constants.version,
204	)
205	if split is not None:	1✔
206	stream = {split: stream[split]}	1✔
207	ds_builder._generators = stream	1✔
208
209	try:	1✔
210	ds_builder.download_and_prepare(	1✔
211	verification_mode="no_checks",
212	download_mode=None if use_cache else "force_redownload",
213	)
214	except DatasetGenerationError as e:	×
215	if e.__cause__:	×
216	raise e.__cause__ from None	×
217	if e.__context__:	×
218	raise e.__context__ from None	×
219	raise	×
220
221	if streaming:	1✔
222	return ds_builder.as_streaming_dataset(split=split)	×
223
224	return ds_builder.as_dataset(split=split)	1✔
225
226	except DatasetGenerationError as e:	×
227	raise e.__cause__	×
228
229
230	def load_dataset(	1✔
231	dataset_query: Optional[str] = None,
232	split: Optional[str] = None,
233	streaming: bool = False,
234	use_cache: Optional[bool] = None,
235	**kwargs,
236	) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
237	"""Loads dataset.
238
239	If the 'dataset_query' argument is provided, then dataset is loaded from a card
240	in local catalog based on parameters specified in the query.
241
242	Alternatively, dataset is loaded from a provided card based on explicitly
243	given parameters.
244
245	If both are given, then the textual recipe is loaded with the key word args overriding the textual recipe args.
246
247	Args:
248	dataset_query (str, optional):
249	A string query which specifies a dataset to load from
250	local catalog or name of specific recipe or benchmark in the catalog. For
251	example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
252	streaming (bool, False):
253	When True yields the data as a stream.
254	This is useful when loading very large datasets.
255	Loading datasets as streams avoid loading all the data to memory, but requires the dataset's loader to support streaming.
256	split (str, optional):
257	The split of the data to load
258	use_cache (bool, optional):
259	If set to True, the returned Huggingface dataset is cached on local disk such that if the same dataset is loaded again, it will be loaded from local disk, resulting in faster runs.
260	If set to False, the returned dataset is not cached.
261	If set to None, the value of this parameter will be determined by setting.dataset_cache_default (default is False).
262	Note that if caching is enabled and the dataset card definition is changed, the old version in the cache may be returned.
263	Enable caching only if you are sure you are working with fixed Unitxt datasets and definitions (e.g. running using predefined datasets from the Unitxt catalog).
264	**kwargs:
265	Arguments used to load dataset from provided card, which is not present in local catalog.
266
267	Returns:
268	DatasetDict
269
270	:Example:
271
272	.. code-block:: python
273
274	dataset = load_dataset(
275	dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
276	) # card and template must be present in local catalog
277
278	# or built programmatically
279	card = TaskCard(...)
280	template = Template(...)
281	loader_limit = 10
282	dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)
283
284	"""
285	recipe = load_recipe(dataset_query, **kwargs)	1✔
286
287	if use_cache is None:	1✔
288	use_cache = settings.dataset_cache_default	1✔
289
290	dataset = _source_to_dataset(	1✔
291	source=recipe, split=split, use_cache=use_cache, streaming=streaming
292	)
293
294	frame = inspect.currentframe()	1✔
295	args, _, _, values = inspect.getargvalues(frame)	1✔
296	all_kwargs = {key: values[key] for key in args if key != "kwargs"}	1✔
297	all_kwargs.update(kwargs)	1✔
298	metadata = fill_metadata(**all_kwargs)	1✔
299	if isinstance(dataset, dict):	1✔
300	for ds in dataset.values():	1✔
301	ds.info.description = metadata.copy()	1✔
302	else:
303	dataset.info.description = metadata	1✔
304	return dataset	1✔
305
306
307	def fill_metadata(**kwargs):	1✔
308	metadata = kwargs.copy()	1✔
309	metadata["unitxt_version"] = get_constants().version	1✔
310	metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]	1✔
311	return metadata	1✔
312
313
314	def evaluate(	1✔
315	predictions: Optional[List[str]] = None,
316	dataset: Union[Dataset, IterableDataset] = None,
317	data=None,
318	calc_confidence_intervals: bool = True,
319	) -> EvaluationResults:
320	if dataset is None and data is None:	1✔
321	raise UnitxtError(message="Specify 'dataset' in evaluate")	×
322	if data is not None:	1✔
323	dataset = data # for backward compatibility	1✔
324	evaluation_result = _compute(	1✔
325	predictions=predictions,
326	references=dataset,
327	calc_confidence_intervals=calc_confidence_intervals,
328	)
329	if hasattr(dataset, "info") and hasattr(dataset.info, "description"):	1✔
330	evaluation_result.metadata["dataset"] = dataset.info.description	1✔
331	if hasattr(predictions, "metadata"):	1✔
332	evaluation_result.metadata["predictions"] = predictions.metadata	×
333	evaluation_result.metadata["creation_time"] = datetime.now().strftime(	1✔
334	"%Y-%m-%d %H:%M:%S.%f"
335	)[:-3]
336	return evaluation_result	1✔
337
338
339	def post_process(predictions, data) -> List[Dict[str, Any]]:	1✔
340	return _inference_post_process(predictions=predictions, references=data)	1✔
341
342
343	@lru_cache_decorator(max_size=128)	1✔
344	def _get_recipe_with_cache(dataset_query: Optional[str] = None, **kwargs):	1✔
345	return load_recipe(dataset_query, **kwargs)	1✔
346
347
348	def produce(	1✔
349	instance_or_instances, dataset_query: Optional[str] = None, **kwargs
350	) -> Union[Dataset, Dict[str, Any]]:
351	is_list = isinstance(instance_or_instances, list)	1✔
352	if not is_list:	1✔
353	instance_or_instances = [instance_or_instances]	1✔
354	dataset_recipe = _get_recipe_with_cache(dataset_query, **kwargs)	1✔
355	result = dataset_recipe.produce(instance_or_instances)	1✔
356	if not is_list:	1✔
357	return result[0]	1✔
358	return Dataset.from_list(result).with_transform(loads_batch)	1✔
359
360
361	def infer(	1✔
362	instance_or_instances,
363	engine: InferenceEngine,
364	dataset_query: Optional[str] = None,
365	return_data: bool = False,
366	return_log_probs: bool = False,
367	return_meta_data: bool = False,
368	previous_messages: Optional[List[Dict[str, str]]] = None,
369	**kwargs,
370	):
371	dataset = produce(instance_or_instances, dataset_query, **kwargs)	1✔
372	if previous_messages is not None:	1✔
373
374	def add_previous_messages(example, index):	1✔
375	example["source"] = previous_messages[index] + example["source"]	1✔
376	return example	1✔
377
378	dataset = dataset.map(add_previous_messages, with_indices=True)	1✔
379	engine, _ = fetch_artifact(engine)	1✔
380	if return_log_probs:	1✔
381	if not isinstance(engine, LogProbInferenceEngine):	×
382	raise NotImplementedError(	×
383	f"Error in infer: return_log_probs set to True but supplied engine "
384	f"{engine.__class__.__name__} does not support logprobs."
385	)
386	infer_outputs = engine.infer_log_probs(dataset, return_meta_data)	×
387	raw_predictions = (	×
388	[output.prediction for output in infer_outputs]
389	if return_meta_data
390	else infer_outputs
391	)
392	raw_predictions = [	×
393	json.dumps(raw_prediction) for raw_prediction in raw_predictions
394	]
395	else:
396	infer_outputs = engine.infer(dataset, return_meta_data)	1✔
397	raw_predictions = (	1✔
398	[output.prediction for output in infer_outputs]
399	if return_meta_data
400	else infer_outputs
401	)
402	predictions = post_process(raw_predictions, dataset)	1✔
403	if return_data:	1✔
404	if return_meta_data:	1✔
405	infer_output_list = [	1✔
406	infer_output.__dict__ for infer_output in infer_outputs
407	]
408	for infer_output in infer_output_list:	1✔
409	del infer_output["prediction"]	1✔
410	dataset = dataset.add_column("infer_meta_data", infer_output_list)	1✔
411	dataset = dataset.add_column("prediction", predictions)	1✔
412	return dataset.add_column("raw_prediction", raw_predictions)	1✔
413	return predictions	1✔
414
415
416	def select(	1✔
417	instance_or_instances,
418	engine: OptionSelectingByLogProbsInferenceEngine,
419	dataset_query: Optional[str] = None,
420	return_data: bool = False,
421	previous_messages: Optional[List[Dict[str, str]]] = None,
422	**kwargs,
423	):
424	dataset = produce(instance_or_instances, dataset_query, **kwargs)	×
425	if previous_messages is not None:	×
426
427	def add_previous_messages(example, index):	×
428	example["source"] = previous_messages[index] + example["source"]	×
429	return example	×
430
431	dataset = dataset.map(add_previous_messages, with_indices=True)	×
432	engine, _ = fetch_artifact(engine)	×
433	predictions = engine.select(dataset)	×
434	# predictions = post_process(raw_predictions, dataset)
435	if return_data:	×
436	return dataset.add_column("prediction", predictions)	×
437	return predictions	×

IBM / unitxt / 26152825991

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