16589358505

Committed 29 Jul 2025 07:17AM UTC coverage: 81.227% (+0.005%) from 81.222%

Build # 16589358505

Build Type

Pull #1880

github

Committed by

web-flow

Commit Message

Merge 2d0af54e7 into 83063f920

Pull Request Pull Request #1880: For load_dataset, use_cache default value is taken from settings

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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, run_post_process=False, verification_mode="no_checks"
        )

    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(	1✔
225	split=split, run_post_process=False, verification_mode="no_checks"
226	)
227
228	except DatasetGenerationError as e:	×
229	raise e.__cause__	×
230
231
232	def load_dataset(	1✔
233	dataset_query: Optional[str] = None,
234	split: Optional[str] = None,
235	streaming: bool = False,
236	use_cache: Optional[bool] = None,
237	**kwargs,
238	) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
239	"""Loads dataset.
240
241	If the 'dataset_query' argument is provided, then dataset is loaded from a card
242	in local catalog based on parameters specified in the query.
243
244	Alternatively, dataset is loaded from a provided card based on explicitly
245	given parameters.
246
247	If both are given, then the textual recipe is loaded with the key word args overriding the textual recipe args.
248
249	Args:
250	dataset_query (str, optional):
251	A string query which specifies a dataset to load from
252	local catalog or name of specific recipe or benchmark in the catalog. For
253	example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
254	streaming (bool, False):
255	When True yields the data as a stream.
256	This is useful when loading very large datasets.
257	Loading datasets as streams avoid loading all the data to memory, but requires the dataset's loader to support streaming.
258	split (str, optional):
259	The split of the data to load
260	use_cache (bool, optional):
261	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.
262	If set to False, the returned dataset is not cached.
263	If set to None, the value of this parameter will be determined by setting.dataset_cache_default (default is False).
264	Note that if caching is enabled and the dataset card definition is changed, the old version in the cache may be returned.
265	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).
266	**kwargs:
267	Arguments used to load dataset from provided card, which is not present in local catalog.
268
269	Returns:
270	DatasetDict
271
272	:Example:
273
274	.. code-block:: python
275
276	dataset = load_dataset(
277	dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
278	) # card and template must be present in local catalog
279
280	# or built programmatically
281	card = TaskCard(...)
282	template = Template(...)
283	loader_limit = 10
284	dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)
285
286	"""
287	recipe = load_recipe(dataset_query, **kwargs)	1✔
288
289	if use_cache is None:	1✔
290	use_cache = settings.dataset_cache_default	1✔
291
292	dataset = _source_to_dataset(	1✔
293	source=recipe, split=split, use_cache=use_cache, streaming=streaming
294	)
295
296	frame = inspect.currentframe()	1✔
297	args, _, _, values = inspect.getargvalues(frame)	1✔
298	all_kwargs = {key: values[key] for key in args if key != "kwargs"}	1✔
299	all_kwargs.update(kwargs)	1✔
300	metadata = fill_metadata(**all_kwargs)	1✔
301	if isinstance(dataset, dict):	1✔
302	for ds in dataset.values():	1✔
303	ds.info.description = metadata.copy()	1✔
304	else:
305	dataset.info.description = metadata	1✔
306	return dataset	1✔
307
308
309	def fill_metadata(**kwargs):	1✔
310	metadata = kwargs.copy()	1✔
311	metadata["unitxt_version"] = get_constants().version	1✔
312	metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]	1✔
313	return metadata	1✔
314
315
316	def evaluate(	1✔
317	predictions: Optional[List[str]] = None,
318	dataset: Union[Dataset, IterableDataset] = None,
319	data=None,
320	calc_confidence_intervals: bool = True,
321	) -> EvaluationResults:
322	if dataset is None and data is None:	1✔
323	raise UnitxtError(message="Specify 'dataset' in evaluate")	×
324	if data is not None:	1✔
325	dataset = data # for backward compatibility	1✔
326	evaluation_result = _compute(	1✔
327	predictions=predictions,
328	references=dataset,
329	calc_confidence_intervals=calc_confidence_intervals,
330	)
331	if hasattr(dataset, "info") and hasattr(dataset.info, "description"):	1✔
332	evaluation_result.metadata["dataset"] = dataset.info.description	1✔
333	if hasattr(predictions, "metadata"):	1✔
334	evaluation_result.metadata["predictions"] = predictions.metadata	×
335	evaluation_result.metadata["creation_time"] = datetime.now().strftime(	1✔
336	"%Y-%m-%d %H:%M:%S.%f"
337	)[:-3]
338	return evaluation_result	1✔
339
340
341	def post_process(predictions, data) -> List[Dict[str, Any]]:	1✔
342	return _inference_post_process(predictions=predictions, references=data)	1✔
343
344
345	@lru_cache_decorator(max_size=128)	1✔
346	def _get_recipe_with_cache(dataset_query: Optional[str] = None, **kwargs):	1✔
347	return load_recipe(dataset_query, **kwargs)	1✔
348
349
350	def produce(	1✔
351	instance_or_instances, dataset_query: Optional[str] = None, **kwargs
352	) -> Union[Dataset, Dict[str, Any]]:
353	is_list = isinstance(instance_or_instances, list)	1✔
354	if not is_list:	1✔
355	instance_or_instances = [instance_or_instances]	1✔
356	dataset_recipe = _get_recipe_with_cache(dataset_query, **kwargs)	1✔
357	result = dataset_recipe.produce(instance_or_instances)	1✔
358	if not is_list:	1✔
359	return result[0]	1✔
360	return Dataset.from_list(result).with_transform(loads_batch)	1✔
361
362
363	def infer(	1✔
364	instance_or_instances,
365	engine: InferenceEngine,
366	dataset_query: Optional[str] = None,
367	return_data: bool = False,
368	return_log_probs: bool = False,
369	return_meta_data: bool = False,
370	previous_messages: Optional[List[Dict[str, str]]] = None,
371	**kwargs,
372	):
373	dataset = produce(instance_or_instances, dataset_query, **kwargs)	1✔
374	if previous_messages is not None:	1✔
375
376	def add_previous_messages(example, index):	1✔
377	example["source"] = previous_messages[index] + example["source"]	1✔
378	return example	1✔
379
380	dataset = dataset.map(add_previous_messages, with_indices=True)	1✔
381	engine, _ = fetch_artifact(engine)	1✔
382	if return_log_probs:	1✔
383	if not isinstance(engine, LogProbInferenceEngine):	×
384	raise NotImplementedError(	×
385	f"Error in infer: return_log_probs set to True but supplied engine "
386	f"{engine.__class__.__name__} does not support logprobs."
387	)
388	infer_outputs = engine.infer_log_probs(dataset, return_meta_data)	×
389	raw_predictions = (	×
390	[output.prediction for output in infer_outputs]
391	if return_meta_data
392	else infer_outputs
393	)
394	raw_predictions = [	×
395	json.dumps(raw_prediction) for raw_prediction in raw_predictions
396	]
397	else:
398	infer_outputs = engine.infer(dataset, return_meta_data)	1✔
399	raw_predictions = (	1✔
400	[output.prediction for output in infer_outputs]
401	if return_meta_data
402	else infer_outputs
403	)
404	predictions = post_process(raw_predictions, dataset)	1✔
405	if return_data:	1✔
406	if return_meta_data:	1✔
407	infer_output_list = [	1✔
408	infer_output.__dict__ for infer_output in infer_outputs
409	]
410	for infer_output in infer_output_list:	1✔
411	del infer_output["prediction"]	1✔
412	dataset = dataset.add_column("infer_meta_data", infer_output_list)	1✔
413	dataset = dataset.add_column("prediction", predictions)	1✔
414	return dataset.add_column("raw_prediction", raw_predictions)	1✔
415	return predictions	1✔
416
417
418	def select(	1✔
419	instance_or_instances,
420	engine: OptionSelectingByLogProbsInferenceEngine,
421	dataset_query: Optional[str] = None,
422	return_data: bool = False,
423	previous_messages: Optional[List[Dict[str, str]]] = None,
424	**kwargs,
425	):
426	dataset = produce(instance_or_instances, dataset_query, **kwargs)	×
427	if previous_messages is not None:	×
428
429	def add_previous_messages(example, index):	×
430	example["source"] = previous_messages[index] + example["source"]	×
431	return example	×
432
433	dataset = dataset.map(add_previous_messages, with_indices=True)	×
434	engine, _ = fetch_artifact(engine)	×
435	predictions = engine.select(dataset)	×
436	# predictions = post_process(raw_predictions, dataset)
437	if return_data:	×
438	return dataset.add_column("prediction", predictions)	×
439	return predictions	×

IBM / unitxt / 16589358505

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