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AutoLabeler

Source code in autolabel/src/autolabel/labeler.py
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class LabelingAgent:
    COST_KEY = "Cost in $"
    CONFIDENCE_MAX_CONTEXT_LENGTH = 3400

    def __init__(
        self,
        config: Union[AutolabelConfig, str, dict],
        cache: Optional[bool] = True,
        example_selector: Optional[BaseExampleSelector] = None,
        create_task: Optional[bool] = False,
        console_output: Optional[bool] = True,
        generation_cache: Optional[BaseCache] = SQLAlchemyGenerationCache(),
        transform_cache: Optional[BaseCache] = SQLAlchemyTransformCache(),
        confidence_cache: Optional[BaseCache] = SQLAlchemyConfidenceCache(),
        confidence_tokenizer: Optional[AutoTokenizer] = None,
    ) -> None:
        self.create_task = create_task
        self.db = StateManager() if self.create_task else None
        self.generation_cache = generation_cache
        self.transform_cache = transform_cache
        self.confidence_cache = confidence_cache
        if not cache:
            logger.warning(
                f"cache parameter is deprecated and will be removed soon. Please use generation_cache, transform_cache and confidence_cache instead."
            )
            self.generation_cache = None
            self.transform_cache = None
            self.confidence_cache = None

        if self.generation_cache is not None:
            self.generation_cache.initialize()
        if self.transform_cache is not None:
            self.transform_cache.initialize()
        if self.confidence_cache is not None:
            self.confidence_cache.initialize()

        self.console = Console(quiet=not console_output)

        self.config = (
            config if isinstance(config, AutolabelConfig) else AutolabelConfig(config)
        )
        self.task = TaskFactory.from_config(self.config)
        self.llm: BaseModel = ModelFactory.from_config(
            self.config, cache=self.generation_cache
        )

        if self.config.confidence_chunk_column():
            if not confidence_tokenizer:
                self.confidence_tokenizer = AutoTokenizer.from_pretrained(
                    "google/flan-t5-xxl"
                )
            else:
                self.confidence_tokenizer = confidence_tokenizer
        score_type = "logprob_average"
        if self.config.task_type() == TaskType.ATTRIBUTE_EXTRACTION:
            score_type = "logprob_average_per_key"
        self.confidence = ConfidenceCalculator(
            score_type=score_type,
            llm=self.llm,
            cache=self.confidence_cache,
        )

        self.example_selector = example_selector

        # Only used if we don't use task management
        self.all_annotations = []

        if self.create_task:
            logger.warning(
                f"create_task parameter is deprecated and will be removed soon. The LLM calls are getting cached and should handle most use cases."
            )

        if in_notebook():
            import nest_asyncio

            nest_asyncio.apply()

    def run(
        self,
        dataset: AutolabelDataset,
        output_name: Optional[str] = None,
        max_items: Optional[int] = None,
        start_index: int = 0,
        additional_metrics: Optional[List[BaseMetric]] = [],
        skip_eval: Optional[bool] = False,
    ) -> Tuple[pd.Series, pd.DataFrame, List[MetricResult]]:
        """Labels data in a given dataset. Output written to new CSV file.

        Args:
            dataset: path to CSV dataset to be annotated
            max_items: maximum items in dataset to be annotated
            output_name: custom name of output CSV file
            start_index: skips annotating [0, start_index)
        """

        dataset = dataset.get_slice(max_items=max_items, start_index=start_index)

        if self.create_task:
            self.db.initialize()
            self.dataset_obj = self.db.initialize_dataset(dataset.df, self.config)
            self.task_object = self.db.initialize_task(self.config)
        else:
            self.all_annotations = []

        if isinstance(dataset, str):
            csv_file_name = (
                output_name
                if output_name
                else f"{dataset.replace('.csv','')}_labeled.csv"
            )
        else:
            csv_file_name = f"{self.config.task_name()}_labeled.csv"

        if self.create_task:
            # Initialize task run and check if it already exists
            self.task_run = self.db.get_task_run(
                self.task_object.id, self.dataset_obj.id
            )
            # Resume/Delete the task if it already exists or create a new task run
            if self.task_run:
                logger.info("Task run already exists.")
                self.task_run = self.handle_existing_task_run(
                    self.task_run,
                    csv_file_name,
                    gt_labels=dataset.gt_labels,
                    additional_metrics=additional_metrics,
                )
            else:
                self.task_run = self.db.create_task_run(
                    csv_file_name, self.task_object.id, self.dataset_obj.id
                )

        # Get the seed examples from the dataset config
        seed_examples = self.config.few_shot_example_set()

        # If this dataset config is a string, read the corrresponding csv file
        if isinstance(seed_examples, str):
            seed_loader = AutolabelDataset(seed_examples, self.config)
            seed_examples = seed_loader.inputs

        # Check explanations are present in data if explanation_column is passed in
        if (
            self.config.explanation_column()
            and len(seed_examples) > 0
            and self.config.explanation_column() not in list(seed_examples[0].keys())
        ):
            raise ValueError(
                f"Explanation column {self.config.explanation_column()} not found in dataset.\nMake sure that explanations were generated using labeler.generate_explanations(seed_file)."
            )

        if self.example_selector is None:
            self.example_selector = ExampleSelectorFactory.initialize_selector(
                self.config,
                [safe_serialize_to_string(example) for example in seed_examples],
                dataset.df.keys().tolist(),
                cache=self.generation_cache is not None,
            )

        if self.config.label_selection():
            if self.config.task_type() != TaskType.CLASSIFICATION:
                self.console.print(
                    "Warning: label_selection only supported for classification tasks!"
                )
            else:
                self.label_selector = LabelSelector.from_examples(
                    labels=self.config.labels_list(),
                    embedding_func=PROVIDER_TO_MODEL.get(
                        self.config.embedding_provider(), DEFAULT_EMBEDDING_PROVIDER
                    )(),
                    k=self.config.label_selection_count(),
                )

        current_index = self.task_run.current_index if self.create_task else 0
        cost = 0.0
        postfix_dict = {}

        indices = range(current_index, len(dataset.inputs))

        for current_index in track_with_stats(
            indices,
            postfix_dict,
            total=len(dataset.inputs) - current_index,
            console=self.console,
        ):
            chunk = dataset.inputs[current_index]

            if self.example_selector:
                examples = self.example_selector.select_examples(
                    safe_serialize_to_string(chunk)
                )
            else:
                examples = []
            # Construct Prompt to pass to LLM
            if (
                self.config.label_selection()
                and self.config.task_type() == TaskType.CLASSIFICATION
            ):
                selected_labels = self.label_selector.select_labels(chunk["example"])
                final_prompt = self.task.construct_prompt(
                    chunk,
                    examples,
                    selected_labels=selected_labels,
                    max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                    get_num_tokens=self.llm.get_num_tokens,
                )
            else:
                final_prompt = self.task.construct_prompt(
                    chunk,
                    examples,
                    max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                    get_num_tokens=self.llm.get_num_tokens,
                )

            response = self.llm.label([final_prompt])
            for i, generations, error, latency in zip(
                range(len(response.generations)),
                response.generations,
                response.errors,
                response.latencies,
            ):
                input_tokens = self.llm.get_num_tokens(final_prompt)
                if error is not None:
                    annotation = LLMAnnotation(
                        successfully_labeled=False,
                        label=self.task.NULL_LABEL_TOKEN,
                        raw_response="",
                        curr_sample=pickle.dumps(chunk),
                        prompt=final_prompt,
                        confidence_score=0,
                        error=error,
                        input_tokens=input_tokens,
                        cost=0,
                        latency=0,
                    )
                else:
                    annotations = []
                    for generation in generations:
                        annotation = self.task.parse_llm_response(
                            generation, chunk, final_prompt
                        )
                        annotation.confidence_prompt = (
                            self.task.construct_confidence_prompt(chunk, examples)
                        )
                        annotation.input_tokens = input_tokens
                        annotation.output_tokens = self.llm.get_num_tokens(
                            annotation.raw_response
                        )
                        annotation.cost = sum(response.costs)
                        annotation.latency = latency

                        if self.config.confidence():
                            try:
                                annotation.confidence_score = self.get_confidence_score(
                                    annotation, chunk, examples
                                )
                            except Exception as e:
                                logger.error(f"Error calculating confidence score: {e}")
                                if (
                                    self.config.task_type()
                                    == TaskType.ATTRIBUTE_EXTRACTION
                                ):
                                    annotation.confidence_score = {}
                                else:
                                    annotation.confidence_score = 0

                        annotations.append(annotation)
                    annotation = self.majority_annotation(annotations)

                # Save the annotation in the database
                self.save_annotation(annotation, current_index, i)

            cost += sum(response.costs)
            postfix_dict[self.COST_KEY] = f"{cost:.2f}"

            # Evaluate the task every eval_every examples
            if not skip_eval and (current_index + 1) % 100 == 0:
                llm_labels = self.get_all_annotations()
                if dataset.gt_labels:
                    eval_result = self.task.eval(
                        llm_labels,
                        dataset.gt_labels[: len(llm_labels)]
                        if isinstance(dataset.gt_labels, list)
                        else {
                            k: v[: len(llm_labels)]
                            for k, v in dataset.gt_labels.items()
                        },
                        additional_metrics=additional_metrics,
                    )

                    for m in eval_result:
                        # This is a row wise metric
                        if isinstance(m.value, list):
                            continue
                        elif m.show_running:
                            postfix_dict[m.name] = (
                                f"{m.value:.4f}"
                                if isinstance(m.value, float)
                                else m.value
                            )

            if self.create_task:
                # Update task run state
                self.task_run = self.save_task_run_state(
                    current_index=current_index + len(chunk)
                )

        llm_labels = self.get_all_annotations()
        eval_result = None
        table = {}

        # if true labels are provided, evaluate accuracy of predictions
        if not skip_eval and dataset.gt_labels:
            eval_result = self.task.eval(
                llm_labels,
                dataset.gt_labels[: len(llm_labels)]
                if isinstance(dataset.gt_labels, list)
                else {k: v[: len(llm_labels)] for k, v in dataset.gt_labels.items()},
                additional_metrics=additional_metrics,
            )
            # TODO: serialize and write to file
            for m in eval_result:
                if isinstance(m.value, list):
                    continue
                elif m.show_running:
                    table[m.name] = m.value
                else:
                    self.console.print(f"{m.name}:\n{m.value}")

        # print cost
        self.console.print(f"Actual Cost: {maybe_round(cost)}")
        print_table(table, console=self.console, default_style=METRIC_TABLE_STYLE)

        dataset.process_labels(llm_labels, eval_result)
        # Only save to csv if output_name is provided or dataset is a string
        if not output_name and isinstance(dataset, str):
            output_name = (
                dataset.rsplit(".", 1)[0] + "_labeled." + dataset.rsplit(".", 1)[1]
            )

        if output_name:
            dataset.save(output_file_name=output_name)
        return dataset

    def plan(
        self,
        dataset: AutolabelDataset,
        max_items: Optional[int] = None,
        start_index: int = 0,
    ) -> None:
        """Calculates and prints the cost of calling autolabel.run() on a given dataset

        Args:
            dataset: path to a CSV dataset
        """
        dataset = dataset.get_slice(max_items=max_items, start_index=start_index)

        if (
            self.config.confidence()
            and "REFUEL_API_KEY" not in os.environ
            and not self.llm.returns_token_probs()
        ):
            raise ValueError(
                "REFUEL_API_KEY environment variable must be set to compute confidence scores. You can request an API key at https://refuel-ai.typeform.com/llm-access."
            )

        prompt_list = []
        total_cost = 0

        # Get the seed examples from the dataset config
        seed_examples = self.config.few_shot_example_set()

        # If this dataset config is a string, read the corrresponding csv file
        if isinstance(seed_examples, str):
            seed_loader = AutolabelDataset(seed_examples, self.config)
            seed_examples = seed_loader.inputs

        # Check explanations are present in data if explanation_column is passed in
        if (
            self.config.explanation_column()
            and len(seed_examples) > 0
            and self.config.explanation_column() not in list(seed_examples[0].keys())
        ):
            raise ValueError(
                f"Explanation column {self.config.explanation_column()} not found in dataset.\nMake sure that explanations were generated using labeler.generate_explanations(seed_file)."
            )

        self.example_selector = ExampleSelectorFactory.initialize_selector(
            self.config,
            [safe_serialize_to_string(example) for example in seed_examples],
            dataset.df.keys().tolist(),
            cache=self.generation_cache is not None,
        )

        if self.config.label_selection():
            if self.config.task_type() != TaskType.CLASSIFICATION:
                self.console.print(
                    "Warning: label_selection only supported for classification tasks!"
                )
            else:
                self.label_selector = LabelSelector.from_examples(
                    labels=self.config.labels_list(),
                    embedding_func=PROVIDER_TO_MODEL.get(
                        self.config.embedding_provider(), DEFAULT_EMBEDDING_PROVIDER
                    )(),
                    k=self.config.label_selection_count(),
                )

        input_limit = min(len(dataset.inputs), 100)

        for input_i in track(
            dataset.inputs[:input_limit],
            description="Generating Prompts...",
            console=self.console,
        ):
            # TODO: Check if this needs to use the example selector
            if self.example_selector:
                examples = self.example_selector.select_examples(
                    safe_serialize_to_string(input_i)
                )
            else:
                examples = []
            if (
                self.config.label_selection()
                and self.config.task_type() == TaskType.CLASSIFICATION
            ):
                selected_labels = self.label_selector.select_labels(input_i["example"])
                final_prompt = self.task.construct_prompt(
                    input_i,
                    examples,
                    selected_labels=selected_labels,
                    max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                    get_num_tokens=self.llm.get_num_tokens,
                )
            else:
                final_prompt = self.task.construct_prompt(
                    input_i,
                    examples,
                    max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                    get_num_tokens=self.llm.get_num_tokens,
                )
            prompt_list.append(final_prompt)

            # Calculate the number of tokens
            curr_cost = self.llm.get_cost(prompt=final_prompt, label="")
            total_cost += curr_cost

        total_cost = total_cost * (len(dataset.inputs) / input_limit)
        table = {
            "Total Estimated Cost": f"${maybe_round(total_cost)}",
            "Number of Examples": len(dataset.inputs),
            "Average cost per example": f"${maybe_round(total_cost / len(dataset.inputs))}",
        }
        table = {"parameter": list(table.keys()), "value": list(table.values())}

        print_table(
            table, show_header=False, console=self.console, styles=COST_TABLE_STYLES
        )
        self.console.rule("Prompt Example")
        self.console.print(f"{prompt_list[0]}", markup=False)
        self.console.rule()

    async def async_run_transform(
        self, transform: BaseTransform, dataset: AutolabelDataset
    ):
        transform_outputs = [
            transform.apply(input_dict) for input_dict in dataset.inputs
        ]

        outputs = await gather_async_tasks_with_progress(
            transform_outputs,
            description=f"Running transform {transform.name()}...",
            console=self.console,
        )
        output_df = pd.DataFrame.from_records(outputs)
        final_df = pd.concat([dataset.df, output_df], axis=1)
        dataset = AutolabelDataset(final_df, self.config)
        return dataset

    def transform(self, dataset: AutolabelDataset):
        transforms = []
        for transform_dict in self.config.transforms():
            transforms.append(
                TransformFactory.from_dict(transform_dict, cache=self.transform_cache)
            )
        for transform in transforms:
            dataset = asyncio.run(self.async_run_transform(transform, dataset))

        return dataset

    def handle_existing_task_run(
        self,
        task_run: TaskRun,
        csv_file_name: str,
        gt_labels: List[str] = None,
        additional_metrics: List[BaseMetric] = [],
    ) -> TaskRun:
        """
        Allows for continuing an existing labeling task. The user will be asked whether they wish to continue from where the run previously left off, or restart from the beginning.
        Args:
            task_run: TaskRun to retry
            csv_file_name: path to the dataset we wish to label (only used if user chooses to restart the task)
            gt_labels: If ground truth labels are provided, performance metrics will be displayed, such as label accuracy
        """
        self.console.print(
            f"There is an existing task with following details: {task_run}"
        )
        llm_labels = self.get_all_annotations()
        if gt_labels and len(llm_labels) > 0:
            pprint("Evaluating the existing task...")
            gt_labels = (
                gt_labels[: len(llm_labels)]
                if isinstance(gt_labels, list)
                else {k: v[: len(llm_labels)] for k, v in gt_labels.items()}
            )
            eval_result = self.task.eval(
                llm_labels, gt_labels, additional_metrics=additional_metrics
            )
            table = {}
            for m in eval_result:
                if isinstance(m.value, list):
                    continue
                elif m.show_running:
                    table[m.name] = m.value
                else:
                    self.console.print(f"{m.name}:\n{m.value}")

            print_table(table, console=self.console, default_style=METRIC_TABLE_STYLE)
        self.console.print(f"{task_run.current_index} examples labeled so far.")
        if not Confirm.ask("Do you want to resume the task?"):
            TaskRunModel.delete_by_id(self.db.session, task_run.id)
            self.console.print("Deleted the existing task and starting a new one...")
            task_run = self.db.create_task_run(
                csv_file_name, self.task_object.id, self.dataset_obj.id
            )
        return task_run

    def get_confidence_score(
        self, annotation: LLMAnnotation, chunk: Dict, examples: List[Dict]
    ) -> Union[float, dict]:
        full_confidence_input = annotation.confidence_prompt + annotation.raw_response
        if (
            self.llm.returns_token_probs()
            or not self.config.confidence_chunk_column()
            or self.get_num_tokens(full_confidence_input)
            < self.CONFIDENCE_MAX_CONTEXT_LENGTH
        ):
            return self.confidence.calculate(model_generation=annotation)
        key_to_chunk = self.config.confidence_chunk_column()
        if not key_to_chunk:
            raise ValueError(
                "confidence_chunk_column must be set in the config to use confidence_chunk_size"
            )
        if key_to_chunk == AUTO_CONFIDENCE_CHUNKING_COLUMN:
            # If the confidence_chunk_column is set to auto,
            # we choose the column with the most tokens as the chunking column.
            max_tokens = -1
            example_template_keys = get_format_variables(self.config.example_template())
            for key in example_template_keys:
                num_tokens = self.get_num_tokens(chunk[key])
                if num_tokens > max_tokens:
                    max_tokens = num_tokens
                    key_to_chunk = key

        empty_chunk = chunk.copy()
        empty_chunk[key_to_chunk] = ""
        empty_prompt = self.task.construct_confidence_prompt(empty_chunk, examples)
        num_tokens_empty_prompt = self.get_num_tokens(empty_prompt)
        num_tokens_per_chunk = (
            self.config.confidence_chunk_size() - num_tokens_empty_prompt
        )
        confidence_chunks = self.chunk_string(chunk[key_to_chunk], num_tokens_per_chunk)

        confidence_scores = []
        for confidence_chunk in confidence_chunks:
            new_chunk = chunk.copy()
            new_chunk[key_to_chunk] = confidence_chunk
            new_prompt = self.task.construct_confidence_prompt(new_chunk, examples)
            annotation_dict = annotation.dict()
            annotation_dict["confidence_prompt"] = new_prompt
            confidence_scores.append(
                self.confidence.calculate(
                    model_generation=LLMAnnotation(**annotation_dict),
                )
            )

        merge_function = MERGE_FUNCTION[self.config.confidence_merge_function()]
        if isinstance(confidence_scores[0], dict):
            merged_confidence = {}
            for key in confidence_scores[0].keys():
                merged_confidence[key] = merge_function(
                    [conf[key] for conf in confidence_scores]
                )
            return merged_confidence
        else:
            merged_confidence = merge_function(confidence_scores)
            return merged_confidence

    def save_task_run_state(
        self, current_index: int = None, status: TaskStatus = "", error: str = ""
    ) -> TaskRun:
        """Saves the current state of the Task being performed"""
        # Save the current state of the task
        if error:
            self.task_run.error = error
        if status:
            self.task_run.status = status
        if current_index:
            self.task_run.current_index = current_index
        return TaskRunModel.update(self.db.session, self.task_run)

    def majority_annotation(
        self, annotation_list: List[LLMAnnotation]
    ) -> LLMAnnotation:
        labels = [a.label for a in annotation_list]
        counts = {}
        for ind, label in enumerate(labels):
            # Needed for named entity recognition which outputs lists instead of strings
            label = str(label)

            if label not in counts:
                counts[label] = (1, ind)
            else:
                counts[label] = (counts[label][0] + 1, counts[label][1])
        max_label = max(counts, key=lambda x: counts[x][0])
        return annotation_list[counts[max_label][1]]

    def generate_explanations(
        self,
        seed_examples: Union[str, List[Dict]],
    ) -> List[Dict]:
        """Use LLM to generate explanations for why examples are labeled the way that they are."""
        out_file = None
        if isinstance(seed_examples, str):
            out_file = seed_examples
            seed_loader = AutolabelDataset(seed_examples, self.config)
            seed_examples = seed_loader.inputs

        explanation_column = self.config.explanation_column()
        if not explanation_column:
            raise ValueError(
                "The explanation column needs to be specified in the dataset config."
            )

        for seed_example in track(
            seed_examples,
            description="Generating explanations",
            console=self.console,
        ):
            explanation_prompt = self.task.get_explanation_prompt(seed_example)
            if self.task.image_col is not None:
                explanation_prompt = json.dumps(
                    {
                        "text": explanation_prompt,
                        "image_url": seed_example[self.task.image_col],
                    }
                )
            explanation = self.llm.label([explanation_prompt])
            explanation = explanation.generations[0][0].text
            seed_example[explanation_column] = str(explanation) if explanation else ""

        if out_file:
            df = pd.DataFrame.from_records(seed_examples)
            df.to_csv(out_file, index=False)

        return seed_examples

    def generate_synthetic_dataset(self) -> AutolabelDataset:
        columns = get_format_variables(self.config.example_template())
        df = pd.DataFrame(columns=columns)
        for label in track(
            self.config.labels_list(),
            description="Generating dataset",
            console=self.console,
        ):
            prompt = self.task.get_generate_dataset_prompt(label)

            result = self.llm.label([prompt])
            if result.errors[0] is not None:
                self.console.print(
                    f"Error generating rows for label {label}: {result.errors[0]}"
                )
            else:
                response = result.generations[0][0].text.strip()

                response = io.StringIO(response)
                label_df = pd.read_csv(response, sep=self.config.delimiter())
                label_df[self.config.label_column()] = label
                df = pd.concat([df, label_df], axis=0, ignore_index=True)
        return AutolabelDataset(df, self.config)

    def clear_cache(self, use_ttl: bool = True):
        """
        Clears the generation and transformation cache from autolabel.
        Args:
            use_ttl: If true, only clears the cache if the ttl has expired.
        """
        self.generation_cache.clear(use_ttl=use_ttl)
        self.transform_cache.clear(use_ttl=use_ttl)

    def save_annotation(self, annotation: LLMAnnotation, current_index: int, i: int):
        if self.create_task:
            # Store the annotation in the database
            AnnotationModel.create_from_llm_annotation(
                self.db.session,
                annotation,
                current_index + i,
                self.task_run.id,
            )
        else:
            self.all_annotations.append(annotation)

    def get_all_annotations(self):
        if self.create_task:
            db_result = AnnotationModel.get_annotations_by_task_run_id(
                self.db.session, self.task_run.id
            )
            return [pickle.loads(a.llm_annotation) for a in db_result]
        else:
            return self.all_annotations

    def get_num_tokens(self, inp: str) -> int:
        """Returns the number of tokens in the prompt"""
        return len(self.confidence_tokenizer.encode(str(inp)))

    def chunk_string(self, inp: str, chunk_size: int) -> List[str]:
        """Chunks the input string into chunks of size chunk_size"""
        tokens = self.confidence_tokenizer.encode(inp)
        chunks = [tokens[i : i + chunk_size] for i in range(0, len(tokens), chunk_size)]
        return [self.confidence_tokenizer.decode(chunk) for chunk in chunks]

chunk_string(inp, chunk_size)

Chunks the input string into chunks of size chunk_size

Source code in autolabel/src/autolabel/labeler.py
def chunk_string(self, inp: str, chunk_size: int) -> List[str]:
    """Chunks the input string into chunks of size chunk_size"""
    tokens = self.confidence_tokenizer.encode(inp)
    chunks = [tokens[i : i + chunk_size] for i in range(0, len(tokens), chunk_size)]
    return [self.confidence_tokenizer.decode(chunk) for chunk in chunks]

clear_cache(use_ttl=True)

Clears the generation and transformation cache from autolabel. Args: use_ttl: If true, only clears the cache if the ttl has expired.

Source code in autolabel/src/autolabel/labeler.py
def clear_cache(self, use_ttl: bool = True):
    """
    Clears the generation and transformation cache from autolabel.
    Args:
        use_ttl: If true, only clears the cache if the ttl has expired.
    """
    self.generation_cache.clear(use_ttl=use_ttl)
    self.transform_cache.clear(use_ttl=use_ttl)

generate_explanations(seed_examples)

Use LLM to generate explanations for why examples are labeled the way that they are.

Source code in autolabel/src/autolabel/labeler.py
def generate_explanations(
    self,
    seed_examples: Union[str, List[Dict]],
) -> List[Dict]:
    """Use LLM to generate explanations for why examples are labeled the way that they are."""
    out_file = None
    if isinstance(seed_examples, str):
        out_file = seed_examples
        seed_loader = AutolabelDataset(seed_examples, self.config)
        seed_examples = seed_loader.inputs

    explanation_column = self.config.explanation_column()
    if not explanation_column:
        raise ValueError(
            "The explanation column needs to be specified in the dataset config."
        )

    for seed_example in track(
        seed_examples,
        description="Generating explanations",
        console=self.console,
    ):
        explanation_prompt = self.task.get_explanation_prompt(seed_example)
        if self.task.image_col is not None:
            explanation_prompt = json.dumps(
                {
                    "text": explanation_prompt,
                    "image_url": seed_example[self.task.image_col],
                }
            )
        explanation = self.llm.label([explanation_prompt])
        explanation = explanation.generations[0][0].text
        seed_example[explanation_column] = str(explanation) if explanation else ""

    if out_file:
        df = pd.DataFrame.from_records(seed_examples)
        df.to_csv(out_file, index=False)

    return seed_examples

get_num_tokens(inp)

Returns the number of tokens in the prompt

Source code in autolabel/src/autolabel/labeler.py
def get_num_tokens(self, inp: str) -> int:
    """Returns the number of tokens in the prompt"""
    return len(self.confidence_tokenizer.encode(str(inp)))

handle_existing_task_run(task_run, csv_file_name, gt_labels=None, additional_metrics=[])

Allows for continuing an existing labeling task. The user will be asked whether they wish to continue from where the run previously left off, or restart from the beginning. Args: task_run: TaskRun to retry csv_file_name: path to the dataset we wish to label (only used if user chooses to restart the task) gt_labels: If ground truth labels are provided, performance metrics will be displayed, such as label accuracy

Source code in autolabel/src/autolabel/labeler.py
def handle_existing_task_run(
    self,
    task_run: TaskRun,
    csv_file_name: str,
    gt_labels: List[str] = None,
    additional_metrics: List[BaseMetric] = [],
) -> TaskRun:
    """
    Allows for continuing an existing labeling task. The user will be asked whether they wish to continue from where the run previously left off, or restart from the beginning.
    Args:
        task_run: TaskRun to retry
        csv_file_name: path to the dataset we wish to label (only used if user chooses to restart the task)
        gt_labels: If ground truth labels are provided, performance metrics will be displayed, such as label accuracy
    """
    self.console.print(
        f"There is an existing task with following details: {task_run}"
    )
    llm_labels = self.get_all_annotations()
    if gt_labels and len(llm_labels) > 0:
        pprint("Evaluating the existing task...")
        gt_labels = (
            gt_labels[: len(llm_labels)]
            if isinstance(gt_labels, list)
            else {k: v[: len(llm_labels)] for k, v in gt_labels.items()}
        )
        eval_result = self.task.eval(
            llm_labels, gt_labels, additional_metrics=additional_metrics
        )
        table = {}
        for m in eval_result:
            if isinstance(m.value, list):
                continue
            elif m.show_running:
                table[m.name] = m.value
            else:
                self.console.print(f"{m.name}:\n{m.value}")

        print_table(table, console=self.console, default_style=METRIC_TABLE_STYLE)
    self.console.print(f"{task_run.current_index} examples labeled so far.")
    if not Confirm.ask("Do you want to resume the task?"):
        TaskRunModel.delete_by_id(self.db.session, task_run.id)
        self.console.print("Deleted the existing task and starting a new one...")
        task_run = self.db.create_task_run(
            csv_file_name, self.task_object.id, self.dataset_obj.id
        )
    return task_run

plan(dataset, max_items=None, start_index=0)

Calculates and prints the cost of calling autolabel.run() on a given dataset

Parameters:

Name Type Description Default
dataset AutolabelDataset

path to a CSV dataset

required
Source code in autolabel/src/autolabel/labeler.py
def plan(
    self,
    dataset: AutolabelDataset,
    max_items: Optional[int] = None,
    start_index: int = 0,
) -> None:
    """Calculates and prints the cost of calling autolabel.run() on a given dataset

    Args:
        dataset: path to a CSV dataset
    """
    dataset = dataset.get_slice(max_items=max_items, start_index=start_index)

    if (
        self.config.confidence()
        and "REFUEL_API_KEY" not in os.environ
        and not self.llm.returns_token_probs()
    ):
        raise ValueError(
            "REFUEL_API_KEY environment variable must be set to compute confidence scores. You can request an API key at https://refuel-ai.typeform.com/llm-access."
        )

    prompt_list = []
    total_cost = 0

    # Get the seed examples from the dataset config
    seed_examples = self.config.few_shot_example_set()

    # If this dataset config is a string, read the corrresponding csv file
    if isinstance(seed_examples, str):
        seed_loader = AutolabelDataset(seed_examples, self.config)
        seed_examples = seed_loader.inputs

    # Check explanations are present in data if explanation_column is passed in
    if (
        self.config.explanation_column()
        and len(seed_examples) > 0
        and self.config.explanation_column() not in list(seed_examples[0].keys())
    ):
        raise ValueError(
            f"Explanation column {self.config.explanation_column()} not found in dataset.\nMake sure that explanations were generated using labeler.generate_explanations(seed_file)."
        )

    self.example_selector = ExampleSelectorFactory.initialize_selector(
        self.config,
        [safe_serialize_to_string(example) for example in seed_examples],
        dataset.df.keys().tolist(),
        cache=self.generation_cache is not None,
    )

    if self.config.label_selection():
        if self.config.task_type() != TaskType.CLASSIFICATION:
            self.console.print(
                "Warning: label_selection only supported for classification tasks!"
            )
        else:
            self.label_selector = LabelSelector.from_examples(
                labels=self.config.labels_list(),
                embedding_func=PROVIDER_TO_MODEL.get(
                    self.config.embedding_provider(), DEFAULT_EMBEDDING_PROVIDER
                )(),
                k=self.config.label_selection_count(),
            )

    input_limit = min(len(dataset.inputs), 100)

    for input_i in track(
        dataset.inputs[:input_limit],
        description="Generating Prompts...",
        console=self.console,
    ):
        # TODO: Check if this needs to use the example selector
        if self.example_selector:
            examples = self.example_selector.select_examples(
                safe_serialize_to_string(input_i)
            )
        else:
            examples = []
        if (
            self.config.label_selection()
            and self.config.task_type() == TaskType.CLASSIFICATION
        ):
            selected_labels = self.label_selector.select_labels(input_i["example"])
            final_prompt = self.task.construct_prompt(
                input_i,
                examples,
                selected_labels=selected_labels,
                max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                get_num_tokens=self.llm.get_num_tokens,
            )
        else:
            final_prompt = self.task.construct_prompt(
                input_i,
                examples,
                max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                get_num_tokens=self.llm.get_num_tokens,
            )
        prompt_list.append(final_prompt)

        # Calculate the number of tokens
        curr_cost = self.llm.get_cost(prompt=final_prompt, label="")
        total_cost += curr_cost

    total_cost = total_cost * (len(dataset.inputs) / input_limit)
    table = {
        "Total Estimated Cost": f"${maybe_round(total_cost)}",
        "Number of Examples": len(dataset.inputs),
        "Average cost per example": f"${maybe_round(total_cost / len(dataset.inputs))}",
    }
    table = {"parameter": list(table.keys()), "value": list(table.values())}

    print_table(
        table, show_header=False, console=self.console, styles=COST_TABLE_STYLES
    )
    self.console.rule("Prompt Example")
    self.console.print(f"{prompt_list[0]}", markup=False)
    self.console.rule()

run(dataset, output_name=None, max_items=None, start_index=0, additional_metrics=[], skip_eval=False)

Labels data in a given dataset. Output written to new CSV file.

Parameters:

Name Type Description Default
dataset AutolabelDataset

path to CSV dataset to be annotated

required
max_items Optional[int]

maximum items in dataset to be annotated

None
output_name Optional[str]

custom name of output CSV file

None
start_index int

skips annotating [0, start_index)

0
Source code in autolabel/src/autolabel/labeler.py
def run(
    self,
    dataset: AutolabelDataset,
    output_name: Optional[str] = None,
    max_items: Optional[int] = None,
    start_index: int = 0,
    additional_metrics: Optional[List[BaseMetric]] = [],
    skip_eval: Optional[bool] = False,
) -> Tuple[pd.Series, pd.DataFrame, List[MetricResult]]:
    """Labels data in a given dataset. Output written to new CSV file.

    Args:
        dataset: path to CSV dataset to be annotated
        max_items: maximum items in dataset to be annotated
        output_name: custom name of output CSV file
        start_index: skips annotating [0, start_index)
    """

    dataset = dataset.get_slice(max_items=max_items, start_index=start_index)

    if self.create_task:
        self.db.initialize()
        self.dataset_obj = self.db.initialize_dataset(dataset.df, self.config)
        self.task_object = self.db.initialize_task(self.config)
    else:
        self.all_annotations = []

    if isinstance(dataset, str):
        csv_file_name = (
            output_name
            if output_name
            else f"{dataset.replace('.csv','')}_labeled.csv"
        )
    else:
        csv_file_name = f"{self.config.task_name()}_labeled.csv"

    if self.create_task:
        # Initialize task run and check if it already exists
        self.task_run = self.db.get_task_run(
            self.task_object.id, self.dataset_obj.id
        )
        # Resume/Delete the task if it already exists or create a new task run
        if self.task_run:
            logger.info("Task run already exists.")
            self.task_run = self.handle_existing_task_run(
                self.task_run,
                csv_file_name,
                gt_labels=dataset.gt_labels,
                additional_metrics=additional_metrics,
            )
        else:
            self.task_run = self.db.create_task_run(
                csv_file_name, self.task_object.id, self.dataset_obj.id
            )

    # Get the seed examples from the dataset config
    seed_examples = self.config.few_shot_example_set()

    # If this dataset config is a string, read the corrresponding csv file
    if isinstance(seed_examples, str):
        seed_loader = AutolabelDataset(seed_examples, self.config)
        seed_examples = seed_loader.inputs

    # Check explanations are present in data if explanation_column is passed in
    if (
        self.config.explanation_column()
        and len(seed_examples) > 0
        and self.config.explanation_column() not in list(seed_examples[0].keys())
    ):
        raise ValueError(
            f"Explanation column {self.config.explanation_column()} not found in dataset.\nMake sure that explanations were generated using labeler.generate_explanations(seed_file)."
        )

    if self.example_selector is None:
        self.example_selector = ExampleSelectorFactory.initialize_selector(
            self.config,
            [safe_serialize_to_string(example) for example in seed_examples],
            dataset.df.keys().tolist(),
            cache=self.generation_cache is not None,
        )

    if self.config.label_selection():
        if self.config.task_type() != TaskType.CLASSIFICATION:
            self.console.print(
                "Warning: label_selection only supported for classification tasks!"
            )
        else:
            self.label_selector = LabelSelector.from_examples(
                labels=self.config.labels_list(),
                embedding_func=PROVIDER_TO_MODEL.get(
                    self.config.embedding_provider(), DEFAULT_EMBEDDING_PROVIDER
                )(),
                k=self.config.label_selection_count(),
            )

    current_index = self.task_run.current_index if self.create_task else 0
    cost = 0.0
    postfix_dict = {}

    indices = range(current_index, len(dataset.inputs))

    for current_index in track_with_stats(
        indices,
        postfix_dict,
        total=len(dataset.inputs) - current_index,
        console=self.console,
    ):
        chunk = dataset.inputs[current_index]

        if self.example_selector:
            examples = self.example_selector.select_examples(
                safe_serialize_to_string(chunk)
            )
        else:
            examples = []
        # Construct Prompt to pass to LLM
        if (
            self.config.label_selection()
            and self.config.task_type() == TaskType.CLASSIFICATION
        ):
            selected_labels = self.label_selector.select_labels(chunk["example"])
            final_prompt = self.task.construct_prompt(
                chunk,
                examples,
                selected_labels=selected_labels,
                max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                get_num_tokens=self.llm.get_num_tokens,
            )
        else:
            final_prompt = self.task.construct_prompt(
                chunk,
                examples,
                max_input_tokens=self.llm.DEFAULT_CONTEXT_LENGTH,
                get_num_tokens=self.llm.get_num_tokens,
            )

        response = self.llm.label([final_prompt])
        for i, generations, error, latency in zip(
            range(len(response.generations)),
            response.generations,
            response.errors,
            response.latencies,
        ):
            input_tokens = self.llm.get_num_tokens(final_prompt)
            if error is not None:
                annotation = LLMAnnotation(
                    successfully_labeled=False,
                    label=self.task.NULL_LABEL_TOKEN,
                    raw_response="",
                    curr_sample=pickle.dumps(chunk),
                    prompt=final_prompt,
                    confidence_score=0,
                    error=error,
                    input_tokens=input_tokens,
                    cost=0,
                    latency=0,
                )
            else:
                annotations = []
                for generation in generations:
                    annotation = self.task.parse_llm_response(
                        generation, chunk, final_prompt
                    )
                    annotation.confidence_prompt = (
                        self.task.construct_confidence_prompt(chunk, examples)
                    )
                    annotation.input_tokens = input_tokens
                    annotation.output_tokens = self.llm.get_num_tokens(
                        annotation.raw_response
                    )
                    annotation.cost = sum(response.costs)
                    annotation.latency = latency

                    if self.config.confidence():
                        try:
                            annotation.confidence_score = self.get_confidence_score(
                                annotation, chunk, examples
                            )
                        except Exception as e:
                            logger.error(f"Error calculating confidence score: {e}")
                            if (
                                self.config.task_type()
                                == TaskType.ATTRIBUTE_EXTRACTION
                            ):
                                annotation.confidence_score = {}
                            else:
                                annotation.confidence_score = 0

                    annotations.append(annotation)
                annotation = self.majority_annotation(annotations)

            # Save the annotation in the database
            self.save_annotation(annotation, current_index, i)

        cost += sum(response.costs)
        postfix_dict[self.COST_KEY] = f"{cost:.2f}"

        # Evaluate the task every eval_every examples
        if not skip_eval and (current_index + 1) % 100 == 0:
            llm_labels = self.get_all_annotations()
            if dataset.gt_labels:
                eval_result = self.task.eval(
                    llm_labels,
                    dataset.gt_labels[: len(llm_labels)]
                    if isinstance(dataset.gt_labels, list)
                    else {
                        k: v[: len(llm_labels)]
                        for k, v in dataset.gt_labels.items()
                    },
                    additional_metrics=additional_metrics,
                )

                for m in eval_result:
                    # This is a row wise metric
                    if isinstance(m.value, list):
                        continue
                    elif m.show_running:
                        postfix_dict[m.name] = (
                            f"{m.value:.4f}"
                            if isinstance(m.value, float)
                            else m.value
                        )

        if self.create_task:
            # Update task run state
            self.task_run = self.save_task_run_state(
                current_index=current_index + len(chunk)
            )

    llm_labels = self.get_all_annotations()
    eval_result = None
    table = {}

    # if true labels are provided, evaluate accuracy of predictions
    if not skip_eval and dataset.gt_labels:
        eval_result = self.task.eval(
            llm_labels,
            dataset.gt_labels[: len(llm_labels)]
            if isinstance(dataset.gt_labels, list)
            else {k: v[: len(llm_labels)] for k, v in dataset.gt_labels.items()},
            additional_metrics=additional_metrics,
        )
        # TODO: serialize and write to file
        for m in eval_result:
            if isinstance(m.value, list):
                continue
            elif m.show_running:
                table[m.name] = m.value
            else:
                self.console.print(f"{m.name}:\n{m.value}")

    # print cost
    self.console.print(f"Actual Cost: {maybe_round(cost)}")
    print_table(table, console=self.console, default_style=METRIC_TABLE_STYLE)

    dataset.process_labels(llm_labels, eval_result)
    # Only save to csv if output_name is provided or dataset is a string
    if not output_name and isinstance(dataset, str):
        output_name = (
            dataset.rsplit(".", 1)[0] + "_labeled." + dataset.rsplit(".", 1)[1]
        )

    if output_name:
        dataset.save(output_file_name=output_name)
    return dataset

save_task_run_state(current_index=None, status='', error='')

Saves the current state of the Task being performed

Source code in autolabel/src/autolabel/labeler.py
def save_task_run_state(
    self, current_index: int = None, status: TaskStatus = "", error: str = ""
) -> TaskRun:
    """Saves the current state of the Task being performed"""
    # Save the current state of the task
    if error:
        self.task_run.error = error
    if status:
        self.task_run.status = status
    if current_index:
        self.task_run.current_index = current_index
    return TaskRunModel.update(self.db.session, self.task_run)