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artifact.py

@@ -5,7 +5,6 @@ import os
 import pkgutil
 import re
 from abc import abstractmethod
-from copy import deepcopy
 from typing import Any, Dict, List, Optional, Tuple, Union, final
 
 from .dataclass import (
@@ -23,7 +22,7 @@ from .parsing_utils import (
 from .settings_utils import get_constants, get_settings
 from .text_utils import camel_to_snake_case, is_camel_case
 from .type_utils import issubtype
-from .utils import artifacts_json_cache, json_dump, save_to_file
+from .utils import artifacts_json_cache, deepcopy, json_dump, save_to_file
 
 logger = get_logger()
 settings = get_settings()

blocks.py

@@ -18,7 +18,7 @@ from .operators import (
 )
 from .processors import ToString, ToStringStripped
 from .recipe import SequentialRecipe
-from .splitters import RandomSampler, SliceSplit, SplitRandomMix, SpreadSplit
+from .splitters import RandomSampler, Sample, SliceSplit, SplitRandomMix
 from .stream import MultiStream
 from .struct_data_operators import (
     ListToKeyValPairs,

collections_operators.py

@@ -1,8 +1,8 @@
-from copy import deepcopy
 from typing import Any, Generator, List, Optional
 
 from .operators import FieldOperator, StreamOperator
 from .stream import Stream
+from .utils import deepcopy
 
 
 class Dictify(FieldOperator):
@@ -100,3 +100,8 @@ class DuplicateBySubLists(StreamOperator):
                         to_field: elements[:i],
                     }
                 yield instance_copy
+
+
+class GetLength(FieldOperator):
+    def process_value(self, collection: Any) -> Any:
+        return len(collection)

data.py

@@ -15,6 +15,7 @@ from .dataset_utils import get_dataset_artifact
 from .deprecation_utils import __file__ as _
 from .dialog_operators import __file__ as _
 from .dict_utils import __file__ as _
+from .error_utils import __file__ as _
 from .eval_utils import __file__ as _
 from .file_utils import __file__ as _
 from .formats import __file__ as _

error_utils.py

@@ -0,0 +1,50 @@
+from typing import Optional
+
+from .logging_utils import get_logger
+
+logger = get_logger()
+
+
+class Documentation:
+    URL = "https://www.unitxt.ai/en/latest/"
+    HUGGINGFACE_METRICS = "docs/adding_metric.html#adding-a-hugginface-metric"
+    ADDING_TASK = "docs/adding_task.html"
+    ADDING_TEMPLATE = "docs/adding_template.html"
+    MULTIPLE_METRICS_OUTPUTS = (
+        "docs/adding_metric.html#metric-outputs-with-multiple-metrics"
+    )
+
+
+def additional_info(path: str) -> str:
+    return f"\nFor more information: see {Documentation.URL}/{path} \n"
+
+
+class UnitxtError(Exception):
+    """Exception raised for Unitxt errors.
+
+    Attributes:
+        message : str -- explanation of the error
+        additional_info_id : Optional[str] -- relative path to additional documentation on web
+        If set, should be one of the DOCUMENATION_* constants in the error_utils.py file.
+
+    """
+
+    def __init__(self, message: str, additional_info_id: Optional[str] = None):
+        if additional_info_id is not None:
+            message += additional_info(additional_info_id)
+        super().__init__(message)
+
+
+class UnitxtWarning:
+    """Object to format warning message to log.
+
+    Attributes:
+        message -- explanation of the warning
+        additional_info_id : Optional[str] -- relative path to additional documentation on web
+        If set, should be one of the DOCUMENATION_* constants in the error_utils.py file.
+    """
+
+    def __init__(self, message: str, additional_info_id: Optional[str] = None):
+        if additional_info_id is not None:
+            message += additional_info(additional_info_id)
+        logger.warning(message)

generator_utils.py

@@ -1,7 +1,7 @@
-import copy
 from typing import Any, Dict, List
 
 from .dataclass import Dataclass, OptionalField
+from .utils import deepcopy
 
 
 class ReusableGenerator(Dataclass):
@@ -22,7 +22,7 @@ class ReusableGenerator(Dataclass):
 class CopyingReusableGenerator(ReusableGenerator):
     def __iter__(self):
         for instance in self.activate():
-            yield copy.deepcopy(instance)
+            yield deepcopy(instance)
 
 
 # if __name__ == "__main__":

inference.py

@@ -5,6 +5,7 @@ from typing import Any, Dict, List, Literal, Optional, Union
 from tqdm import tqdm
 
 from .artifact import Artifact
+from .dataclass import InternalField
 from .deprecation_utils import deprecation
 from .logging_utils import get_logger
 from .operator import PackageRequirementsMixin
@@ -376,13 +377,11 @@ class WMLInferenceEngine(
     """Runs inference using ibm-watsonx-ai.
 
     Attributes:
-        client: By default, it is created by a class instance but can be directly
-            provided instead as an instance of 'ibm_watsonx_ai.client.APIClient'.
-        credentials: By default, it is created by a class instance which tries to retrieve
-            proper environment variables ("WML_URL", "WML_PROJECT_ID", "WML_APIKEY").
-            However, either a dictionary with the following keys: "url", "apikey",
-            "project_id", or an instance of 'ibm_watsonx_ai.credentials.Credentials'
-            can be directly provided instead.
+        credentials (Dict[str, str], optional): By default, it is created by a class
+            instance which tries to retrieve proper environment variables
+            ("WML_URL", "WML_PROJECT_ID", "WML_APIKEY"). However, a dictionary with
+            the following keys: "url", "apikey", "project_id" can be directly provided
+            instead.
         model_name (str, optional): ID of a model to be used for inference. Mutually
             exclusive with 'deployment_id'.
         deployment_id (str, optional): Deployment ID of a tuned model to be used for
@@ -412,8 +411,7 @@ class WMLInferenceEngine(
         results = wml_inference.infer(dataset["test"])
     """
 
-    client: Any = None
-    credentials: Any = None
+    credentials: Optional[Dict[Literal["url", "apikey", "project_id"], str]] = None
     model_name: Optional[str] = None
     deployment_id: Optional[str] = None
     label: str = "wml"
@@ -422,11 +420,40 @@ class WMLInferenceEngine(
         "It is advised to have Python version >=3.10 installed, as at lower version this package "
         "may cause conflicts with other installed packages."
     }
-    data_classification_policy = ["proprietary"]
+    data_classification_policy = ["public", "proprietary"]
     parameters: Optional[WMLInferenceEngineParams] = None
 
+    _client: Any = InternalField(default=None, name="WML client")
+
+    def verify(self):
+        super().verify()
+
+        if self.credentials is not None:
+            for key in self.credentials:
+                if key not in ["url", "apikey", "project_id"]:
+                    raise ValueError(
+                        f'Illegal credential key: {key}, use only ["url", "apikey", "project_id"]'
+                    )
+
+        assert (
+            self.model_name
+            or self.deployment_id
+            and not (self.model_name and self.deployment_id)
+        ), "Either 'model_name' or 'deployment_id' must be specified, but not both at the same time."
+
+    def process_data_before_dump(self, data):
+        if "credentials" in data:
+            for key, value in data["credentials"].items():
+                if key != "url":
+                    data["credentials"][key] = "<hidden>"
+                else:
+                    data["credentials"][key] = value
+        return data
+
     @staticmethod
-    def _read_wml_credentials_from_env() -> Dict[str, str]:
+    def _read_wml_credentials_from_env() -> (
+        Dict[Literal["url", "apikey", "project_id"], str]
+    ):
         credentials = {}
         for env_var_name in ["WML_URL", "WML_PROJECT_ID", "WML_APIKEY"]:
             env_var = os.environ.get(env_var_name)
@@ -453,32 +480,20 @@ class WMLInferenceEngine(
         return client
 
     def prepare(self):
-        if self.client is None:
-            self.client = self._initialize_wml_client()
+        self._client = self._initialize_wml_client()
 
         self._set_inference_parameters()
 
-    def verify(self):
-        assert (
-            self.model_name
-            or self.deployment_id
-            and not (self.model_name and self.deployment_id)
-        ), "Either 'model_name' or 'deployment_id' must be specified, but not both at the same time."
-        super().verify()
-
     def _infer(self, dataset):
         from ibm_watsonx_ai.foundation_models import ModelInference
 
         model = ModelInference(
             model_id=self.model_name,
             deployment_id=self.deployment_id,
-            api_client=self.client,
+            api_client=self._client,
         )
 
-        return [
-            model.generate_text(
-                prompt=instance["source"],
-                params=self.to_dict([WMLInferenceEngineParamsMixin], keep_empty=False),
-            )
-            for instance in dataset
-        ]
+        return model.generate_text(
+            prompt=dataset["source"],
+            params=self.to_dict([WMLInferenceEngineParamsMixin], keep_empty=False),
+        )

loaders.py

@@ -36,7 +36,6 @@ import itertools
 import os
 import tempfile
 from abc import abstractmethod
-from copy import deepcopy
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
@@ -54,6 +53,7 @@ from .operators import Set
 from .settings_utils import get_settings
 from .stream import DynamicStream, MultiStream
 from .type_utils import isoftype
+from .utils import deepcopy
 
 logger = get_logger()
 settings = get_settings()

metric.py

@@ -14,6 +14,7 @@ from .dataset_utils import __file__ as _
 from .deprecation_utils import __file__ as _
 from .dialog_operators import __file__ as _
 from .dict_utils import __file__ as _
+from .error_utils import __file__ as _
 from .eval_utils import __file__ as _
 from .file_utils import __file__ as _
 from .formats import __file__ as _

metric_utils.py

@@ -1,5 +1,4 @@
 import json
-from copy import deepcopy
 from typing import Any, Dict, Generator, Iterable, List, Optional
 
 from datasets import Features, Value
@@ -27,6 +26,7 @@ from .schema import UNITXT_DATASET_SCHEMA
 from .settings_utils import get_settings
 from .stream import DynamicStream, MultiStream
 from .struct_data_operators import LoadJson
+from .utils import deepcopy
 
 
 class MultiStreamScoreMean(MultiStreamOperator):

metrics.py

@@ -1,15 +1,14 @@
 import ast
 import json
+import os
 import re
 import string
 import uuid
 import warnings
 from abc import ABC, abstractmethod
 from collections import Counter, defaultdict
-from copy import deepcopy
 from dataclasses import field
 from operator import itemgetter
-from statistics import mean
 from typing import Any, Dict, Generator, List, Optional, Tuple, Union
 
 import evaluate
@@ -22,11 +21,13 @@ from scipy.stats._warnings_errors import DegenerateDataWarning
 from .artifact import Artifact, fetch_artifact
 from .dataclass import (
     AbstractField,
+    DeprecatedField,
     InternalField,
     NonPositionalField,
     OptionalField,
 )
 from .deprecation_utils import deprecation
+from .error_utils import Documentation, UnitxtWarning
 from .inference import HFPipelineBasedInferenceEngine, InferenceEngine
 from .logging_utils import get_logger
 from .metric_utils import InstanceInput, MetricRequest, MetricResponse
@@ -42,6 +43,7 @@ from .random_utils import get_seed
 from .settings_utils import get_settings
 from .stream import MultiStream, Stream
 from .type_utils import Type, isoftype, parse_type_string, to_type_string
+from .utils import deepcopy
 
 logger = get_logger()
 settings = get_settings()
@@ -141,13 +143,25 @@ class Metric(Artifact):
             else score_name
         )
 
-    def _add_score_prefixes_to_score_dict(self, scores: Dict[str, Any]):
+    def _add_score_prefixes_to_score_dict_and_check_against_existing_scores(
+        self, scores: Dict[str, Any], existing_scores: Dict[str, Any]
+    ) -> Dict[str, Any]:
         new_scores = {}
         for score_name, score in scores.items():
             score_with_prefix = self._add_score_prefix(score_name)
             new_scores[score_with_prefix] = (
                 score if score_name not in ["score_name"] else self.score_prefix + score
             )
+        for new_score_name in new_scores:
+            if new_score_name in ["score", "score_name"]:
+                continue
+            if new_score_name in existing_scores:
+                UnitxtWarning(
+                    message=f"Metric '{new_score_name}' that has just been evaluated to {new_scores[new_score_name]}, is already recorded "
+                    f"to have value {existing_scores[new_score_name]} by a previous metric evaluation on this instance or stream. "
+                    f"To avoid overwriting the existing value, add a score_prefix to the metric (e.g. score_prefix='my_second_').",
+                    additional_info_id=Documentation.MULTIPLE_METRICS_OUTPUTS,
+                )
         return new_scores
 
     def _validate_references_and_prediction(self, references, predictions):
@@ -238,12 +252,14 @@ class Metric(Artifact):
     def disable_confidence_interval_calculation(self):
         pass
 
-    # update instance["score"]["global"] with the newly computed global score, global_score, for the
-    # current metric computed.  global_score contains "score" and "score_name" fields that reflect
-    # (the main_score of) the current metric.
+    # update instance["score"]["global"] with the global_score just computed for the
+    # current metric.  global_score contains "score" and "score_name" fields that reflect
+    # (the main_score of) the current metric. If CI was computed for global_score, then global_score
+    # also contains "score_ci_low" and "score_ci_high" that reflect (the main_score of) the current metric.
     # A simple python-dictionary-update adds new fields to instance["score"]["global"], and also replaces the values
-    # of its fields "score" and "score_name", to reflect the current metric, overwriting previous metrics' settings
-    # of these fields (if any previous metric exists).
+    # of its fields "score" and "score_name" (and "score_ci_low", "score_ci_high" if applicable),
+    # to reflect the current metric, overwriting previous metrics' settings of these fields
+    # (if any previous metric exists).
     # When global_score does NOT contain ci score (because CI was not computed for the current metric), but
     # one of the previous metrics computed did have, the last of such previous metrics set the values in
     # fields "score_ci_low" and "score_ci_high" in instance["score"]["global"] to reflect its
@@ -254,15 +270,25 @@ class Metric(Artifact):
     # therefore, not consistent with "score_name".
     # In such a case, following the python-dictionary-update, we pop out fields "score_ci_low" and
     # "score_ci_high" from instance["score"]["global"], so that now all the fields "score.." in
-    # instance["score"]["global"] are consistent with the current metric: The current metric
-    # is named instance["score"]["global"]["score_name"], its score shows in
+    # instance["score"]["global"] are consistent with the current metric: The metric that is named
+    # instance["score"]["global"]["score_name"], its score shows in
     # field instance["score"]["global"]["score"], and it does not have ci_scores,
     # which is also reflected in the absence of fields "score_ci_low" and "score_ci_high" from instance["score"]["global"].
     # If ci IS computed for the current metric, global_score contains "score_ci_low" and "score_ci_high", and these overwrite
-    # the ones existing in instance["score"]["global"] by a simple python-dictionary-update, and no need for any further fixeup.
+    # the ones existing in instance["score"]["global"] by the simple python-dictionary-update, and no need for any further fixeup.
     def update_and_adjust_global_score(
         self, instance: Dict[str, Any], global_score: dict
     ):
+        for score_name in global_score:
+            if score_name in ["score", "score_name", "score_ci_low", "score_ci_high"]:
+                continue
+            if score_name in instance["score"]["global"]:
+                UnitxtWarning(
+                    message=f"Global metric '{score_name}' that has just been evaluated to {global_score[score_name]}, is already recorded "
+                    f"to have value {instance['score']['global'][score_name]} by a previous metric evaluation on this stream. "
+                    f"To avoid overwriting the value, add a score_prefix to the metric (e.g. score_prefix='my_{score_name}'.",
+                    additional_info_id=Documentation.MULTIPLE_METRICS_OUTPUTS,
+                )
         instance["score"]["global"].update(global_score)
         for score_ci in ["score_ci_low", "score_ci_high"]:
             if score_ci in global_score:
@@ -559,12 +585,18 @@ class GlobalMetric(StreamOperator, MetricWithConfidenceInterval):
                     instance_score[self.main_score] = no_score_value
 
             instance["score"]["instance"].update(
-                self._add_score_prefixes_to_score_dict(instance_score)
+                self._add_score_prefixes_to_score_dict_and_check_against_existing_scores(
+                    instance_score, instance["score"]["instance"]
+                )
             )
         self._validate_references_and_prediction(references, predictions)
 
         result = self._compute(references, predictions, task_data)
-        global_score.update(self._add_score_prefixes_to_score_dict(result))
+        global_score.update(
+            self._add_score_prefixes_to_score_dict_and_check_against_existing_scores(
+                result, global_score
+            )
+        )
         score_name = global_score["score_name"]
         confidence_interval = self.compute_global_confidence_intervals(
             references, predictions, task_data, score_name
@@ -657,7 +689,9 @@ class BulkInstanceMetric(StreamOperator, MetricWithConfidenceInterval):
                 instance["score"] = {"global": {}, "instance": {}}
 
             instance["score"]["instance"].update(
-                self._add_score_prefixes_to_score_dict(score)
+                self._add_score_prefixes_to_score_dict_and_check_against_existing_scores(
+                    score, instance["score"]["instance"]
+                )
             )
             instances.append(instance)
 
@@ -669,7 +703,7 @@ class BulkInstanceMetric(StreamOperator, MetricWithConfidenceInterval):
             if reduction == "mean":
                 for field_name in fields:
                     field_name_with_prefix = self._add_score_prefix(field_name)
-                    global_score[field_name_with_prefix] = mean(
+                    global_score[field_name_with_prefix] = nan_mean(
                         [
                             instance["score"]["instance"][field_name_with_prefix]
                             for instance in instances
@@ -1140,7 +1174,9 @@ class InstanceMetric(StreamOperator, MetricWithConfidenceInterval):
                 instance["score"] = {"global": {}, "instance": {}}
 
             instance["score"]["instance"].update(
-                self._add_score_prefixes_to_score_dict(instance_score)
+                self._add_score_prefixes_to_score_dict_and_check_against_existing_scores(
+                    instance_score, instance["score"]["instance"]
+                )
             )
 
             instances.append(instance)
@@ -1326,7 +1362,6 @@ class StringContainment(InstanceMetric):
     ci_scores = ["string_containment"]
 
     prediction_type = Any  # string representation is compared
-    single_reference_per_prediction = False  # multiple references allowed
 
     def compute(
         self, references: List[Any], prediction: Any, task_data: List[Dict]
@@ -1341,11 +1376,59 @@ class StringContainment(InstanceMetric):
         return result
 
 
+class StringContainmentRatio(InstanceMetric):
+    """Metric that returns the ratio of values from a specific field contained in the prediction.
+
+    Attributes:
+        field: The field from the task_data that contains the values to be checked for containment.
+               Example task:
+                    Task(
+                        input_fields={"question": str},
+                        reference_fields={"entities": str},
+                        prediction_type=str,
+                        metrics=["string_containment_ratio[field=entities]"],
+                    )
+    """
+
+    reduction_map = {"mean": ["string_containment"]}
+    main_score = "string_containment"
+    ci_scores = ["string_containment"]
+    field: str = None
+
+    prediction_type = Any  # string representation is compared
+
+    def compute(
+        self, references: List[Any], prediction: Any, task_data: List[Dict]
+    ) -> dict:
+        if self.field not in task_data:
+            raise ValueError(
+                f"'{self.field}' field required by {__class__.__name__} is not in passed in task_data: {task_data}"
+            )
+        contain_results = [
+            str(value) in str(prediction) for value in task_data[self.field]
+        ]
+        score = sum(contain_results) / len(contain_results)
+        result = {self.main_score: score}
+        result["score"] = result[self.main_score]
+        result["score_name"] = self.main_score
+        return result
+
+    def verify(self):
+        super().verify()
+        if self.field is None:
+            raise ValueError(
+                "StringContainmentRatio metric requires the 'field' attribute to be set."
+            )
+
+
 class MetricPipeline(MultiStreamOperator, Metric):
     main_score: str = None
     preprocess_steps: Optional[List[StreamingOperator]] = field(default_factory=list)
-    postpreprocess_steps: Optional[List[StreamingOperator]] = field(
-        default_factory=list
+    postprocess_steps: Optional[List[StreamingOperator]] = field(default_factory=list)
+    postpreprocess_steps: Optional[List[StreamingOperator]] = DeprecatedField(
+        metadata={
+            "deprecation_msg": "Field 'postpreprocess_steps' is deprecated. Please use 'postprocess_steps' for the same purpose."
+        }
     )
     metric: Metric = None
 
@@ -1366,6 +1449,23 @@ class MetricPipeline(MultiStreamOperator, Metric):
 
     def prepare(self):
         super().prepare()
+        has_postpreprocess = (
+            hasattr(self, "postpreprocess_steps")
+            and self.postpreprocess_steps is not None
+            and isinstance(self.postpreprocess_steps, list)
+            and len(self.postpreprocess_steps) > 0
+        )
+        has_postprocess = (
+            hasattr(self, "postprocess_steps")
+            and self.postprocess_steps is not None
+            and isinstance(self.postprocess_steps, list)
+            and len(self.postprocess_steps) > 0
+        )
+        assert not (
+            has_postpreprocess and has_postprocess
+        ), "Must define at most one of postpreprocess_steps (which is deprecated) and postprocess_steps (to be used from now on)"
+        if has_postpreprocess:
+            self.postprocess_steps = self.postpreprocess_steps
         self.prepare_score = Copy(
             field_to_field=[
                 [
@@ -1383,7 +1483,7 @@ class MetricPipeline(MultiStreamOperator, Metric):
         for step in self.preprocess_steps:
             multi_stream = step(multi_stream)
         multi_stream = self.metric(multi_stream)
-        for step in self.postpreprocess_steps:
+        for step in self.postprocess_steps:
             multi_stream = step(multi_stream)
         return self.prepare_score(multi_stream)
 
@@ -1409,6 +1509,13 @@ class HuggingfaceMetric(GlobalMetric):
     experiment_id: str = OptionalField(default_factory=lambda: str(uuid.uuid4()))
 
     def verify(self):
+        if os.path.exists(self.hf_metric_name):
+            UnitxtWarning(
+                f"{self.get_metric_name()} uses a huggingface metric {self.hf_metric_name} which is defined in a local file."
+                f"This may cause issues when running on different machine or different root directories.",
+                Documentation.HUGGINGFACE_METRICS,
+            )
+
         assert (
             self.hf_additional_input_fields is None
             or isoftype(self.hf_additional_input_fields, List[str])
@@ -1654,7 +1761,7 @@ class F1(GlobalMetric):
             average=self.average,
         )
         if isinstance(result[self.metric], numpy.ndarray):
-            final_result = {self.main_score: mean(result[self.metric])}
+            final_result = {self.main_score: nan_mean(result[self.metric])}
             for i, label in enumerate(labels):
                 final_result[f"{self.metric}_" + self.id_to_str[label]] = result[
                     self.metric
@@ -1959,7 +2066,7 @@ class F1MultiLabel(GlobalMetric):
             assert (
                 len(result[self.metric]) == len(labels)
             ), f"F1 result ({result[self.metric]}) has more entries than labels ({labels})"
-            final_result = {self.main_score: mean(result[self.metric])}
+            final_result = {self.main_score: nan_mean(result[self.metric])}
             for i, label in enumerate(labels):
                 final_result[self.metric + "_" + label] = result[self.metric][i]
         else:
@@ -2001,7 +2108,17 @@ class F1MacroMultiLabel(F1MultiLabel):
     average = None
 
 
-class Rouge(InstanceMetric):
+class NLTKMixin(Artifact):
+    def prepare(self):
+        super().prepare()
+        import nltk
+
+        nltk.download("punkt", quiet=True)
+        nltk.download("punkt_tab", quiet=True)
+        self.nltk = nltk
+
+
+class Rouge(InstanceMetric, NLTKMixin):
     main_score = "rougeL"
     prediction_type = str
     single_reference_per_prediction = False  # multiple references allowed
@@ -2014,21 +2131,17 @@ class Rouge(InstanceMetric):
 
     def prepare(self):
         super().prepare()
-        import nltk
         from rouge_score import rouge_scorer
 
         self.rouge_scorer = rouge_scorer
 
-        nltk.download("punkt", quiet=True)
-        self.sent_tokenize = nltk.sent_tokenize
-
     def compute(self, references: List[Any], prediction: Any, task_data: Dict) -> dict:
         # for a single instance, prediction is of type str, and references: list of str
         if self.sent_split_newline:
-            prediction = "\n".join(self.sent_tokenize(prediction.strip()))
+            prediction = "\n".join(self.nltk.sent_tokenize(prediction.strip()))
 
             references = [
-                "\n".join(self.sent_tokenize(reference.strip()))
+                "\n".join(self.nltk.sent_tokenize(reference.strip()))
                 for reference in references
             ]
 
@@ -2044,7 +2157,7 @@ class Rouge(InstanceMetric):
         return score
 
 
-class RougeHF(HuggingfaceInstanceMetric):
+class RougeHF(HuggingfaceInstanceMetric, NLTKMixin):
     hf_metric_name = "rouge"
     main_score = "rougeL"
     scale = 1.0
@@ -2070,18 +2183,13 @@ class RougeHF(HuggingfaceInstanceMetric):
             {"use_aggregator": False, "rouge_types": self.rouge_types}
         )
 
-        import nltk
-
-        nltk.download("punkt", quiet=True)
-        self.sent_tokenize = nltk.sent_tokenize
-
     def compute(self, references, prediction, task_data: List[Dict]):
         # for a single instance, prediction is of type str, and references: list of str
         if self.sent_split_newline:
-            prediction = "\n".join(self.sent_tokenize(prediction.strip()))
+            prediction = "\n".join(self.nltk.sent_tokenize(prediction.strip()))
 
             references = [
-                "\n".join(self.sent_tokenize(reference.strip()))
+                "\n".join(self.nltk.sent_tokenize(reference.strip()))
                 for reference in references
             ]
 
@@ -3360,7 +3468,7 @@ class NDCG(GlobalMetric):
                     for pred in q_predictions
                 ]
             scores.append(self.eval([q_references], [q_predictions]))
-        return {self.main_score: mean(scores) if len(scores) > 0 else np.nan}
+        return {self.main_score: nan_mean(scores) if len(scores) > 0 else np.nan}
 
 
 class RetrievalMetric(InstanceMetric):
@@ -3695,8 +3803,8 @@ def performance_drop_rate(
     if any(len(scores) == 0 for scores in group_scores_list):
         # no comparison can be made since there is not at least one score per type
         return np.nan
-    control_mean = mean(group_scores_list[0])
-    comparison_mean = mean(group_scores_list[1])
+    control_mean = nan_mean(group_scores_list[0])
+    comparison_mean = nan_mean(group_scores_list[1])
     if control_mean == 0:
         # return 0 if comparison is also 0
         if comparison_mean == 0:
@@ -3809,8 +3917,8 @@ def normalized_cohens_h(
         # no comparison can be made since there is not at least one score per type
         h, norm_h = np.nan, np.nan
     else:
-        control_mean = mean(group_scores_list[0])
-        comparison_mean = mean(group_scores_list[1])
+        control_mean = nan_mean(group_scores_list[0])
+        comparison_mean = nan_mean(group_scores_list[1])
         h = 2 * (np.arcsin(np.sqrt(comparison_mean)) - np.arcsin(np.sqrt(control_mean)))
         norm_h = np.clip(a=h / np.pi, a_min=-1, a_max=1)
 
@@ -3863,7 +3971,7 @@ def normalized_hedges_g(
         g, norm_g = np.nan, np.nan
     else:
         # otherwise, calculate the variances
-        group_mean = [mean(scores) for scores in group_scores_list]
+        group_mean = [nan_mean(scores) for scores in group_scores_list]
         # sample variance with 1 degree of freedom (denominator n-1); if n=1, return 0 since otherwise throws an error
         group_var = [
             0.0 if nn == 1 else np.var(scores, ddof=1)
@@ -3922,7 +4030,7 @@ def mean_subgroup_score(
     if len(score_list) == 0:
         # no scores to use
         return np.nan
-    return mean(score_list)
+    return nan_mean(score_list)
 
 
 # metrics using mean reduction

operators.py

@@ -45,7 +45,6 @@ import uuid
 import zipfile
 from abc import abstractmethod
 from collections import Counter, defaultdict
-from copy import deepcopy
 from dataclasses import field
 from itertools import zip_longest
 from random import Random
@@ -86,7 +85,7 @@ from .settings_utils import get_settings
 from .stream import DynamicStream, Stream
 from .text_utils import nested_tuple_to_string
 from .type_utils import isoftype
-from .utils import flatten_dict
+from .utils import deepcopy, flatten_dict
 
 settings = get_settings()
 

schema.py

@@ -1,9 +1,9 @@
 import json
-from dataclasses import field
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 from datasets import Features, Sequence, Value
 
+from .artifact import Artifact
 from .operator import InstanceOperatorValidator
 
 UNITXT_DATASET_SCHEMA = Features(
@@ -20,10 +20,7 @@ UNITXT_DATASET_SCHEMA = Features(
 )
 
 
-class ToUnitxtGroup(InstanceOperatorValidator):
-    group: str
-    metrics: List[str] = None
-    postprocessors: List[str] = field(default_factory=lambda: ["to_string_stripped"])
+class Finalize(InstanceOperatorValidator):
     remove_unnecessary_fields: bool = True
 
     @staticmethod
@@ -43,6 +40,7 @@ class ToUnitxtGroup(InstanceOperatorValidator):
                 "template": self.artifact_to_jsonable(
                     instance["recipe_metadata"]["template"]
                 ),
+                "num_demos": instance["recipe_metadata"]["num_demos"],
             },
         }
         instance["task_data"] = json.dumps(task_data)
@@ -56,11 +54,16 @@ class ToUnitxtGroup(InstanceOperatorValidator):
 
             for key in keys_to_delete:
                 del instance[key]
-        instance["group"] = self.group
-        if self.metrics is not None:
-            instance["metrics"] = self.metrics
-        if self.postprocessors is not None:
-            instance["postprocessors"] = self.postprocessors
+        if "group" not in instance:
+            instance["group"] = "unitxt"
+        instance["metrics"] = [
+            metric.to_json() if isinstance(metric, Artifact) else metric
+            for metric in instance["metrics"]
+        ]
+        instance["postprocessors"] = [
+            processor.to_json() if isinstance(processor, Artifact) else processor
+            for processor in instance["postprocessors"]
+        ]
         return instance
 
     def validate(self, instance: Dict[str, Any], stream_name: Optional[str] = None):

splitters.py

@@ -1,6 +1,5 @@
 import itertools
 from abc import abstractmethod
-from copy import deepcopy
 from difflib import get_close_matches
 from typing import Dict, List, Optional
 
@@ -17,6 +16,7 @@ from .split_utils import (
 )
 from .stream import EmptyStreamError, FaultyStreamError, MultiStream
 from .type_utils import isoftype
+from .utils import deepcopy
 
 
 class Splitter(MultiStreamOperator):
@@ -109,36 +109,25 @@ class SliceSplit(Splitter):
         return MultiStream.from_generators(generators)
 
 
-class Sampler(Artifact):
-    sample_size: int = None
-
-    def prepare(self):
-        super().prepare()
-        self.set_size(self.sample_size)
+def get_random_generator_based_on_instance(instance):
+    return new_random_generator(sub_seed={**instance["input_fields"]})
 
-    def set_size(self, size):
-        if isinstance(size, str):
-            assert (
-                size.isdigit()
-            ), f"sample_size must be a natural number, got {self.sample_size}"
-            size = int(size)
-        self.sample_size = size
 
+class Sampler(Artifact):
     @abstractmethod
     def sample(
-        self, instances_pool: List[Dict[str, object]], instance: Dict[str, object]
+        self,
+        sample_size: int,
+        instances_pool: List[Dict[str, object]],
+        instance: Dict[str, object],
     ) -> List[Dict[str, object]]:
         pass
 
-    def get_random_generator_based_on_instance(self, instance):
-        return new_random_generator(sub_seed={**instance["input_fields"]})
-
     def filter_source_by_instance(
         self, instances_pool: List[Dict[str, object]], instance: Dict[str, object]
     ) -> List[Dict[str, object]]:
         if "input_fields" not in instance:
             raise ValueError(f"'input_fields' field is missing from '{instance}'.")
-        # l = list(filter(lambda x: x["inputs"] != instance["inputs"], instances_pool))
         try:
             return [
                 item
@@ -154,12 +143,13 @@ class RandomSampler(Sampler):
 
     def sample(
         self,
+        sample_size,
         instances_pool: List[Dict[str, object]],
         instance: Optional[Dict[str, object]],
     ) -> List[Dict[str, object]]:
         instances_pool = list(instances_pool)
-        random_generator = self.get_random_generator_based_on_instance(instance)
-        return random_generator.sample(instances_pool, self.sample_size)
+        random_generator = get_random_generator_based_on_instance(instance)
+        return random_generator.sample(instances_pool, sample_size)
 
 
 class FixedIndicesSampler(Sampler):
@@ -175,13 +165,14 @@ class FixedIndicesSampler(Sampler):
 
     def sample(
         self,
+        sample_size,
         instances_pool: List[Dict[str, object]],
         instance: Optional[Dict[str, object]],
     ) -> List[Dict[str, object]]:
         num_instances = len(instances_pool)
 
         instances = []
-        for index in self.indices[0 : self.sample_size]:
+        for index in self.indices[0:sample_size]:
             if index >= num_instances:
                 raise ValueError(
                     f"FixedIndicesSampler 'indices' field contains index ({index}) which is out of bounds of the instance pool ( of size {num_instances})"
@@ -200,7 +191,10 @@ class CloseTextSampler(Sampler):
     field: str
 
     def sample(
-        self, instances_pool: List[Dict[str, object]], instance: Dict[str, object]
+        self,
+        sample_size: int,
+        instances_pool: List[Dict[str, object]],
+        instance: Dict[str, object],
     ) -> List[Dict[str, object]]:
         field = f"input_fields/{self.field}"
         value = dict_get(instance, field)
@@ -211,9 +205,7 @@ class CloseTextSampler(Sampler):
         options = []
         for instance_in_pool in instances_pool:
             options.append(dict_get(instance_in_pool, field))
-        closest_matches = get_close_matches(
-            value, options, n=self.sample_size, cutoff=0
-        )
+        closest_matches = get_close_matches(value, options, n=sample_size, cutoff=0)
         # Randmly select 'sample_size' instances that are from the closest matches text
         # (There may be multiple instance with same text in the given field, and the order returned is
         # is also randomized )
@@ -222,8 +214,8 @@ class CloseTextSampler(Sampler):
             for instance_in_pool in instances_pool
             if dict_get(instance_in_pool, field) in closest_matches
         ]
-        random_generator = self.get_random_generator_based_on_instance(instance)
-        return random_generator.sample(instances_pool, self.sample_size)
+        random_generator = get_random_generator_based_on_instance(instance)
+        return random_generator.sample(instances_pool, sample_size)
 
 
 class DiverseLabelsSampler(Sampler):
@@ -306,26 +298,27 @@ class DiverseLabelsSampler(Sampler):
 
     def sample(
         self,
+        sample_size: int,
         instances_pool: List[Dict[str, object]],
         instance: Optional[Dict[str, object]],
     ) -> List[Dict[str, object]]:
         if self.labels_cache is None:
             self.labels_cache = self.divide_by_repr(instances_pool)
         all_labels = list(self.labels_cache.keys())
-        random_generator = self.get_random_generator_based_on_instance(instance)
+        random_generator = get_random_generator_based_on_instance(instance)
         random_generator.shuffle(all_labels)
         from collections import Counter
 
-        if self.sample_size > len(instances_pool):
+        if sample_size > len(instances_pool):
             raise ValueError(
-                f"Request sample size {self.sample_size} is greater than number of instances {len(instances_pool)}"
+                f"Request sample size {sample_size} is greater than number of instances {len(instances_pool)}"
             )
         total_allocated = 0
         allocations = Counter()
 
-        while total_allocated < self.sample_size:
+        while total_allocated < sample_size:
             for label in all_labels:
-                if total_allocated < self.sample_size:
+                if total_allocated < sample_size:
                     if len(self.labels_cache[label]) - allocations[label] > 0:
                         allocations[label] += 1
                         total_allocated += 1
@@ -341,40 +334,56 @@ class DiverseLabelsSampler(Sampler):
         return result
 
 
-class SpreadSplit(InstanceOperatorWithMultiStreamAccess):
-    source_stream: str = None
-    target_field: str = None
-    sampler: Sampler = None
+class Sample(InstanceOperatorWithMultiStreamAccess):
+    from_stream: str
+    to_field: str
+    sampler: Sampler
 
     def prepare(self):
         self.local_cache = None
         self.sampler.prepare()
 
-    def verify(self):
-        assert self.source_stream is not None, "Source stream must be specified"
-        assert self.target_field is not None, "Target field must be specified"
-        assert self.sampler is not None, "Sampler must be specified"
-        return super().verify()
+    @abstractmethod
+    def get_sample_size(self, instance) -> int:
+        pass
 
     def process(
         self, instance: Dict[str, object], multi_stream: MultiStream
     ) -> Dict[str, object]:
+        sample_size = self.get_sample_size(instance)
         try:
             if self.local_cache is None:
-                self.local_cache = deepcopy(list(multi_stream[self.source_stream]))
+                self.local_cache = deepcopy(list(multi_stream[self.from_stream]))
 
             source_stream = self.local_cache
             source_stream = self.sampler.filter_source_by_instance(
                 source_stream, instance
             )
-            if len(source_stream) < self.sampler.sample_size:
+            if len(source_stream) < sample_size:
                 raise ValueError(
                     f"Size of population to sample from: {len(source_stream)} is smaller than the needed sample_size: {self.sampler.sample_size}."
                 )
-            sampled_instances = self.sampler.sample(source_stream, instance)
-            instance[self.target_field] = sampled_instances
+            sampled_instances = self.sampler.sample(
+                sample_size=sample_size, instances_pool=source_stream, instance=instance
+            )
+            instance[self.to_field] = sampled_instances
             return instance
         except FaultyStreamError as e:
             raise EmptyStreamError(
-                f"Unable to fetch instances from '{self.source_stream}' to '{self.target_field}', due to {e.__class__.__name__}: {e}"
+                f"Unable to fetch instances from '{self.from_stream}' to '{self.to_field}', due to {e.__class__.__name__}: {e}"
             ) from e
+
+
+class ConstantSizeSample(Sample):
+    sample_size: int
+
+    def get_sample_size(self, instance) -> int:
+        return self.sample_size
+
+
+class RandomSizeSample(Sample):
+    sample_sizes: List[int]
+
+    def get_sample_size(self, instance) -> int:
+        random_generator = get_random_generator_based_on_instance(instance)
+        return random_generator.choice(self.sample_sizes)

standard.py

@@ -1,17 +1,18 @@
-from typing import List
+from typing import List, Optional, Union
 
 from .card import TaskCard
+from .collections_operators import GetLength
 from .dataclass import Field, InternalField, NonPositionalField, OptionalField
 from .formats import Format, SystemFormat
 from .logging_utils import get_logger
 from .operator import SequentialOperator, SourceSequentialOperator, StreamingOperator
 from .operators import Augmentor, NullAugmentor, Set, StreamRefiner
 from .recipe import Recipe
-from .schema import ToUnitxtGroup
-from .splitters import Sampler, SeparateSplit, SpreadSplit
+from .schema import Finalize
+from .splitters import ConstantSizeSample, RandomSizeSample, Sampler, SeparateSplit
 from .stream import MultiStream
 from .system_prompts import EmptySystemPrompt, SystemPrompt
-from .templates import Template
+from .templates import ApplyRandomTemplate, ApplySingleTemplate, Template
 
 logger = get_logger()
 
@@ -21,15 +22,15 @@ class CreateDemosPool(SeparateSplit):
     pass
 
 
-class AddDemosField(SpreadSplit):
-    pass
-
-
 class BaseRecipe(Recipe, SourceSequentialOperator):
+    # Base parameters
     card: TaskCard
-    template: Template = None
+    template: Union[Template, List[Template]] = None
     system_prompt: SystemPrompt = Field(default_factory=EmptySystemPrompt)
     format: Format = Field(default_factory=SystemFormat)
+
+    # Additional parameters
+    template_card_index: int = NonPositionalField(default=None)
     metrics: List[str] = NonPositionalField(default=None)
     postprocessors: List[str] = NonPositionalField(default=None)
 
@@ -44,7 +45,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
     test_refiner: StreamRefiner = OptionalField(default_factory=StreamRefiner)
 
     demos_pool_size: int = None
-    num_demos: int = 0
+    num_demos: Optional[Union[int, List[int]]] = 0
     demos_removed_from_data: bool = True
 
     demos_pool_name: str = "demos_pool"
@@ -59,16 +60,22 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
     def before_process_multi_stream(self):
         super().before_process_multi_stream()
 
+    @property
+    def max_demos_size(self):
+        if isinstance(self.num_demos, list):
+            return max(self.num_demos)
+        return self.num_demos
+
     def verify(self):
         super().verify()
-        if self.num_demos > 0:
+        if self.use_demos:
             if self.demos_pool_size is None or self.demos_pool_size < 1:
                 raise ValueError(
                     "When using demonstrations both num_demos and demos_pool_size should be assigned with positive integers."
                 )
-            if self.demos_pool_size < self.num_demos:
+            if self.demos_pool_size < self.max_demos_size:
                 raise ValueError(
-                    f"num_demos (got: {self.num_demos}) should not exceed demos_pool_size (got: {self.demos_pool_size})"
+                    f"num_demos (got: {self.max_demos_size}) should not exceed demos_pool_size (got: {self.demos_pool_size})"
                 )
             if self.loader_limit and self.demos_pool_size > self.loader_limit:
                 raise ValueError(
@@ -105,6 +112,17 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
                 f"post processors must be a list of post processor.  Got postprocessors = {self.postprocessors}"
             )
 
+        if self.template is None:
+            raise ValueError(
+                "You must set in the recipe either `template`, `template_card_index` or `templates`."
+            )
+
+        if isinstance(self.template, list):
+            for template in self.template:
+                self.verify_template(template)
+        else:
+            self.verify_template(self.template)
+
     def prepare_refiners(self):
         self.train_refiner.max_instances = self.max_train_instances
         self.train_refiner.apply_to_streams = ["train"]
@@ -118,31 +136,12 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
         self.test_refiner.apply_to_streams = ["test"]
         self.processing.steps.append(self.test_refiner)
 
-    def prepare_metrics_and_postprocessors(self):
-        # Check is done here to ensure get_postprocessor is called on
-        # a Template object
-        if self.template is not None and not isinstance(self.template, Template):
+    def verify_template(self, template):
+        if not isinstance(template, Template):
             raise ValueError(
-                f"template argument must be an object of type Template.  Got template = {self.template}"
+                f"template argument must be an object of type Template. Got template = {template}"
             )
 
-        if self.postprocessors is None:
-            postprocessors = self.template.get_postprocessors()
-        else:
-            postprocessors = self.postprocessors
-
-        if self.metrics is None:
-            metrics = self.card.task.metrics
-        else:
-            metrics = self.metrics
-
-        metrics = [
-            metric if isinstance(metric, str) else metric.to_json()
-            for metric in metrics
-        ]
-
-        return metrics, postprocessors
-
     def set_pipelines(self):
         self.loading = SequentialOperator()
         self.loading.__description__ = "Loading the data from the data source."
@@ -158,8 +157,8 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
         self.processing.__description__ = (
             "Setting task fields (and selecting demos per sample if needed)."
         )
-        self.verblization = SequentialOperator()
-        self.verblization.__description__ = "Verbalizing the input to the model and gold references to the 'source', 'target' and 'references' fields."
+        self.verbalization = SequentialOperator()
+        self.verbalization.__description__ = "Verbalizing the input to the model and gold references to the 'source', 'target' and 'references' fields."
         self.finalize = SequentialOperator()
         self.finalize.__description__ = "Adding post processors. Removing intermediate fields. Creating the final output dataset."
 
@@ -169,7 +168,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
             self.standardization,
             self.processing,
             self.metadata,
-            self.verblization,
+            self.verbalization,
             self.finalize,
         ]
 
@@ -193,7 +192,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
 
         self.inference = SequentialOperator()
 
-        self.inference.steps = [self.verblization, self.finalize]
+        self.inference.steps = [self.verbalization, self.finalize]
 
         self._demos_pool_cache = None
 
@@ -202,7 +201,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
         return list(self.inference_instance(ms)["__inference__"])
 
     def production_demos_pool(self):
-        if self.num_demos > 0:
+        if self.use_demos:
             if self._demos_pool_cache is None:
                 self._demos_pool_cache = list(
                     self.inference_demos()[self.demos_pool_name]
@@ -210,6 +209,14 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
             return self._demos_pool_cache
         return []
 
+    @property
+    def has_custom_demos_pool(self):
+        return self.demos_pool_size is not None and self.demos_pool_size > 0
+
+    @property
+    def use_demos(self):
+        return self.num_demos is not None and self.max_demos_size > 0
+
     def produce(self, task_instances):
         """Use the recipe in production to produce model ready query from standard task instance."""
         self.before_process_multi_stream()
@@ -243,11 +250,8 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
         self.metadata.steps.append(
             Set(
                 fields={
-                    "recipe_metadata": {
-                        "template": self.template,
-                        "system_prompt": self.system_prompt,
-                        "format": self.format,
-                    }
+                    "recipe_metadata/system_prompt": self.system_prompt,
+                    "recipe_metadata/format": self.format,
                 }
             )
         )
@@ -260,7 +264,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
             self.augmentor.set_task_input_fields(self.card.task.augmentable_inputs)
             self.processing.steps.append(self.augmentor)
 
-        if self.demos_pool_size is not None and self.demos_pool_size > 0:
+        if self.has_custom_demos_pool:
             self.processing.steps.append(
                 CreateDemosPool(
                     from_split=self.demos_taken_from,
@@ -270,7 +274,7 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
                 )
             )
 
-        if self.num_demos > 0:
+        if self.use_demos:
             if self.sampler is None:
                 if self.card.sampler is None:
                     raise ValueError(
@@ -279,33 +283,76 @@ class BaseRecipe(Recipe, SourceSequentialOperator):
                     )
                 self.sampler = self.card.sampler
 
-            self.sampler.set_size(self.num_demos)
-
         self.prepare_refiners()
 
-        self.verblization.steps.append(self.template)
-        if self.num_demos > 0:
-            self.verblization.steps.append(
-                AddDemosField(
-                    source_stream=self.demos_pool_name,
-                    target_field=self.demos_field,
-                    sampler=self.sampler,
+        if self.use_demos:
+            if isinstance(self.num_demos, int):
+                self.verbalization.steps.append(
+                    ConstantSizeSample(
+                        from_stream=self.demos_pool_name,
+                        to_field=self.demos_field,
+                        sampler=self.sampler,
+                        sample_size=self.num_demos,
+                    )
+                )
+                self.verbalization.steps.append(
+                    Set(fields={"recipe_metadata/num_demos": self.num_demos})
+                )
+
+            elif isinstance(self.num_demos, list):
+                self.verbalization.steps.append(
+                    RandomSizeSample(
+                        from_stream=self.demos_pool_name,
+                        to_field=self.demos_field,
+                        sampler=self.sampler,
+                        sample_sizes=self.num_demos,
+                    )
                 )
+                self.verbalization.steps.append(
+                    GetLength(field="demos", to_field="recipe_metadata/num_demos")
+                )
+            else:
+                raise ValueError("num_demos must be int or List[int]")
+
+            if isinstance(self.template, list):
+                self.verbalization.steps.append(
+                    ApplyRandomTemplate(
+                        templates=self.template, demos_field=self.demos_field
+                    )
+                )
+            else:
+                self.verbalization.steps.append(
+                    ApplySingleTemplate(
+                        template=self.template, demos_field=self.demos_field
+                    )
+                )
+        else:
+            self.verbalization.steps.append(
+                Set(fields={"recipe_metadata/num_demos": 0})
             )
-        self.verblization.steps.append(self.system_prompt)
-        self.verblization.steps.append(self.format)
-        if self.augmentor.augment_model_input:
-            self.verblization.steps.append(self.augmentor)
+            if isinstance(self.template, list):
+                self.verbalization.steps.append(
+                    ApplyRandomTemplate(templates=self.template)
+                )
+            else:
+                self.verbalization.steps.append(
+                    ApplySingleTemplate(template=self.template)
+                )
 
-        metrics, postprocessors = self.prepare_metrics_and_postprocessors()
+        self.verbalization.steps.append(self.system_prompt)
+        self.verbalization.steps.append(self.format)
+        if self.augmentor.augment_model_input:
+            self.verbalization.steps.append(self.augmentor)
 
-        self.finalize.steps.append(
-            ToUnitxtGroup(
-                group="unitxt",
-                metrics=metrics,
-                postprocessors=postprocessors,
+        if self.postprocessors is not None:
+            self.finalize.steps.append(
+                Set(fields={"postprocessors": self.postprocessors})
             )
-        )
+
+        if self.metrics is not None:
+            self.finalize.steps.append(Set(fields={"metrics": self.metrics}))
+
+        self.finalize.steps.append(Finalize())
 
 
 class StandardRecipeWithIndexes(BaseRecipe):

stream.py

@@ -2,7 +2,6 @@ import tempfile
 import traceback
 import warnings
 from abc import abstractmethod
-from copy import deepcopy
 from typing import Any, Callable, Dict, Generator, Iterable, List
 
 from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
@@ -11,6 +10,7 @@ from .dataclass import Dataclass, OptionalField
 from .generator_utils import CopyingReusableGenerator, ReusableGenerator
 from .logging_utils import get_logger
 from .settings_utils import get_settings
+from .utils import deepcopy
 
 settings = get_settings()
 logger = get_logger()

struct_data_operators.py

@@ -18,7 +18,6 @@ For key-value pairs, expected input format is:
 import json
 import random
 from abc import ABC, abstractmethod
-from copy import deepcopy
 from typing import (
     Any,
     Dict,
@@ -30,6 +29,7 @@ import pandas as pd
 
 from .dict_utils import dict_get
 from .operators import FieldOperator, InstanceOperator
+from .utils import deepcopy
 
 
 class SerializeTable(ABC, FieldOperator):

task.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional, Union
 from .artifact import fetch_artifact
 from .dataclass import DeprecatedField
 from .deprecation_utils import deprecation
-from .logging_utils import get_logger
+from .error_utils import Documentation, UnitxtError, UnitxtWarning
 from .operator import InstanceOperator
 from .type_utils import (
     Type,
@@ -77,12 +77,14 @@ class Task(InstanceOperator):
     def prepare(self):
         super().prepare()
         if self.input_fields is not None and self.inputs is not None:
-            raise ValueError(
-                "Conflicting attributes: 'input_fields' cannot be set simultaneously with 'inputs'. Use only 'input_fields'"
+            raise UnitxtError(
+                "Conflicting attributes: 'input_fields' cannot be set simultaneously with 'inputs'. Use only 'input_fields'",
+                Documentation.ADDING_TASK,
             )
         if self.reference_fields is not None and self.outputs is not None:
-            raise ValueError(
-                "Conflicting attributes: 'reference_fields' cannot be set simultaneously with 'output'. Use only 'reference_fields'"
+            raise UnitxtError(
+                "Conflicting attributes: 'reference_fields' cannot be set simultaneously with 'output'. Use only 'reference_fields'",
+                Documentation.ADDING_TASK,
             )
 
         self.input_fields = (
@@ -107,9 +109,15 @@ class Task(InstanceOperator):
 
     def verify(self):
         if self.input_fields is None:
-            raise ValueError("Missing attribute in task: 'input_fields' not set.")
+            raise UnitxtError(
+                "Missing attribute in task: 'input_fields' not set.",
+                Documentation.ADDING_TASK,
+            )
         if self.reference_fields is None:
-            raise ValueError("Missing attribute in task: 'reference_fields' not set.")
+            raise UnitxtError(
+                "Missing attribute in task: 'reference_fields' not set.",
+                Documentation.ADDING_TASK,
+            )
         for io_type in ["input_fields", "reference_fields"]:
             data = (
                 self.input_fields
@@ -118,11 +126,12 @@ class Task(InstanceOperator):
             )
 
             if isinstance(data, list) or not is_type_dict(data):
-                get_logger().warning(
+                UnitxtWarning(
                     f"'{io_type}' field of Task should be a dictionary of field names and their types. "
                     f"For example, {{'text': str, 'classes': List[str]}}. Instead only '{data}' was "
                     f"passed. All types will be assumed to be 'Any'. In future version of unitxt this "
-                    f"will raise an exception."
+                    f"will raise an exception.",
+                    Documentation.ADDING_TASK,
                 )
                 data = {key: Any for key in data}
                 if io_type == "input_fields":
@@ -131,11 +140,12 @@ class Task(InstanceOperator):
                     self.reference_fields = data
 
         if not self.prediction_type:
-            get_logger().warning(
+            UnitxtWarning(
                 "'prediction_type' was not set in Task. It is used to check the output of "
                 "template post processors is compatible with the expected input of the metrics. "
                 "Setting `prediction_type` to 'Any' (no checking is done). In future version "
-                "of unitxt this will raise an exception."
+                "of unitxt this will raise an exception.",
+                Documentation.ADDING_TASK,
             )
             self.prediction_type = Any
 
@@ -191,18 +201,20 @@ class Task(InstanceOperator):
             ):
                 continue
 
-            raise ValueError(
+            raise UnitxtError(
                 f"The task's prediction type ({prediction_type}) and '{metric_id}' "
-                f"metric's prediction type ({metric_prediction_type}) are different."
+                f"metric's prediction type ({metric_prediction_type}) are different.",
+                Documentation.ADDING_TASK,
             )
 
     def verify_defaults(self):
         if self.defaults:
             if not isinstance(self.defaults, dict):
-                raise ValueError(
+                raise UnitxtError(
                     f"If specified, the 'defaults' must be a dictionary, "
                     f"however, '{self.defaults}' was provided instead, "
-                    f"which is of type '{to_type_string(type(self.defaults))}'."
+                    f"which is of type '{to_type_string(type(self.defaults))}'.",
+                    Documentation.ADDING_TASK,
                 )
 
             for default_name, default_value in self.defaults.items():

templates.py

@@ -6,17 +6,20 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 from .artifact import Artifact
 from .collections import ListCollection
 from .dataclass import NonPositionalField
+from .dict_utils import dict_set
+from .error_utils import Documentation, UnitxtError
 from .operator import InstanceOperator
 from .random_utils import new_random_generator
 from .type_utils import isoftype
 
 
-class TemplateFormatKeyError(KeyError):
+class TemplateFormatKeyError(UnitxtError):
     def __init__(self, template, data, data_type, format_str, format_name):
         keys = ", ".join(data.keys())
         super().__init__(
             f"Available {data_type}s are [{keys}] "
-            f"but {template.__class__.__name__}.{format_name} format requires a different ones: '{format_str}'"
+            f"but {template.__class__.__name__}.{format_name} format requires a different ones: '{format_str}'",
+            Documentation.ADDING_TEMPLATE,
         )
 
 
@@ -92,6 +95,7 @@ class Template(InstanceOperator):
             "references": references,
             "instruction": instruction,
             "target_prefix": target_prefix,
+            "postprocessors": self.postprocessors,
         }
 
     @abstractmethod
@@ -108,9 +112,6 @@ class Template(InstanceOperator):
     ) -> Tuple[str, List[str]]:
         pass
 
-    def get_postprocessors(self) -> List[str]:
-        return self.postprocessors
-
     def serialize_data(self, data):
         return {
             k: ", ".join(str(t) for t in v) if isinstance(v, list) else v
@@ -123,6 +124,11 @@ class Template(InstanceOperator):
         if serialize:
             data = self.serialize_data(data)
         try:
+            if format_str is None:
+                raise UnitxtError(
+                    f"Required field 'output_format' of class {self.__class__.__name__} not set in {self.__class__.__name__}",
+                    Documentation.ADDING_TEMPLATE,
+                )
             return format_str.format(**data)
         except KeyError as e:
             raise TemplateFormatKeyError(
@@ -130,6 +136,49 @@ class Template(InstanceOperator):
             ) from e
 
 
+class ApplyTemplate(InstanceOperator):
+    demos_field: Optional[str] = None
+
+    @abstractmethod
+    def get_template(self, instance: Dict[str, Any]) -> Template:
+        pass
+
+    def apply(self, template: Template, instance: Dict[str, Any]):
+        return template.process_instance(instance)
+
+    def process(
+        self, instance: Dict[str, Any], stream_name: Optional[str] = None
+    ) -> Dict[str, Any]:
+        template = self.get_template(instance)
+
+        if self.demos_field is not None:
+            if self.demos_field not in instance:
+                raise ValueError("Demos field is missing.")
+            instance[self.demos_field] = [
+                self.apply(template, demo_instance)
+                for demo_instance in instance[self.demos_field]
+            ]
+        dict_set(instance, "recipe_metadata/template", template)
+        return self.apply(template, instance)
+
+
+class ApplySingleTemplate(ApplyTemplate):
+    template: Template
+
+    def get_template(self, instance: Dict[str, Any]) -> Template:
+        return self.template
+
+
+class ApplyRandomTemplate(ApplyTemplate):
+    templates: List[Template]
+
+    def get_template(self, instance: Dict[str, Any]) -> Template:
+        random_generator = new_random_generator(
+            {**instance["input_fields"], **instance["reference_fields"]}
+        )
+        return random_generator.choice(self.templates)
+
+
 class InputOutputTemplate(Template):
     """Generate field 'source' from fields designated as input, and fields 'target' and 'references' from fields designated as output, of the processed instance.
 
@@ -471,8 +520,9 @@ class MultipleChoiceTemplate(Template):
             try:
                 return reference_fields[self.choices_field].index(target)
             except ValueError as e:
-                raise ValueError(
-                    f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}"
+                raise UnitxtError(
+                    f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}",
+                    Documentation.ADDING_TEMPLATE,
                 ) from e
         return target
 
@@ -485,8 +535,9 @@ class MultipleChoiceTemplate(Template):
             try:
                 target = reference_fields[self.choices_field].index(target)
             except ValueError as e:
-                raise ValueError(
-                    f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}"
+                raise UnitxtError(
+                    f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {reference_fields[self.choices_field]}",
+                    Documentation.ADDING_TEMPLATE,
                 ) from e
 
         choices = self.inputs_to_choices(reference_fields, self.target_choice_format)
@@ -494,8 +545,9 @@ class MultipleChoiceTemplate(Template):
         try:
             target = choices[target]
         except IndexError as e:
-            raise IndexError(
-                f"MultipleChoiceTemplate cannot find index number {target} in choices: {choices}"
+            raise UnitxtError(
+                f"MultipleChoiceTemplate cannot find index number {target} in choices: {choices}",
+                Documentation.ADDING_TEMPLATE,
             ) from e
 
         return target, [target]
@@ -574,21 +626,21 @@ class YesNoTemplate(Template):
         try:
             gold_class_names = reference_fields[self.label_field]
         except KeyError as e:
-            raise RuntimeError(
+            raise UnitxtError(
                 f"Available reference_fields are {list(reference_fields.keys())}, missing required label field: '{self.label_field}'."
             ) from e
         if not isinstance(gold_class_names, list):
-            raise RuntimeError(
+            raise UnitxtError(
                 f"Unexpected value for gold_class_names: '{gold_class_names}'. Expecting a list."
             )
         try:
             queried_class_name = reference_fields[self.class_field]
         except KeyError as e:
-            raise RuntimeError(
+            raise UnitxtError(
                 f"Available reference_fields are {list(reference_fields.keys())}, missing required class field: '{self.class_field}'."
             ) from e
         if not queried_class_name or not isinstance(queried_class_name, str):
-            raise RuntimeError(
+            raise UnitxtError(
                 f"Unexpected value for queried_class_names: '{queried_class_name}'. Expected a string."
             )
         if queried_class_name in gold_class_names:
@@ -674,8 +726,9 @@ class MultiLabelTemplate(InputOutputTemplate):
     ) -> str:
         labels = reference_fields[self.labels_field]
         if not isinstance(labels, list):
-            raise ValueError(
-                f"MultiLabelTemplate requires labels field '{self.labels_field}' to be a list. Got {self.labels_field}<{type(labels).__name__}>: {labels}"
+            raise UnitxtError(
+                f"MultiLabelTemplate requires labels field '{self.labels_field}' to be a list. Got {self.labels_field}<{type(labels).__name__}>: {labels}",
+                Documentation.ADDING_TEMPLATE,
             )
         if len(labels) == 0:
             labels = [self.empty_label]
@@ -694,12 +747,14 @@ class MultiReferenceTemplate(InputOutputTemplate):
     ) -> List[str]:
         references = reference_fields[self.references_field]
         if not isoftype(references, List[str]):
-            raise ValueError(
-                f"MultiReferenceTemplate requires references field '{self.references_field}' to be List[str]. Got {self.references_field}<{type(references).__name__}>: {references}"
+            raise UnitxtError(
+                f"MultiReferenceTemplate requires references field '{self.references_field}' to be List[str]. Got {self.references_field}<{type(references).__name__}>: {references}",
+                Documentation.ADDING_TEMPLATE,
             )
         if len(references) == 0:
-            raise ValueError(
-                "No references found. MultiReferenceTemplate requires at least one reference."
+            raise UnitxtError(
+                "No references found. MultiReferenceTemplate requires at least one reference.",
+                Documentation.ADDING_TEMPLATE,
             )
 
         if self.random_reference:

utils.py

@@ -1,3 +1,4 @@
+import copy
 import importlib.util
 import json
 import os
@@ -125,3 +126,7 @@ def import_module_from_file(file_path):
     # Load the module
     spec.loader.exec_module(module)
     return module
+
+
+def deepcopy(obj):
+    return copy.deepcopy(obj)

version.py

@@ -1 +1 @@
-version = "1.12.2"
+version = "1.12.3"