Request

Make requests to the API.

request(text=None, output=None, ids=None, text_column=None, id_column=None, files=None, directory=None, file_type='txt', encoding=None, return_text=False, api_args=None, frameworks=None, framework_prefix=None, bundle_size=1000, bundle_byte_limit=7500000.0, collapse_lines=False, retry_limit=50, clear_cache=False, request_cache=True, cores=1, in_memory=None, verbose=False, progress_bar=os.getenv('RECEPTIVITI_PB', 'True'), overwrite=False, make_request=True, text_as_paths=False, dotenv=True, cache=os.getenv('RECEPTIVITI_CACHE', ''), cache_overwrite=False, cache_format=os.getenv('RECEPTIVITI_CACHE_FORMAT', ''), key=os.getenv('RECEPTIVITI_KEY', ''), secret=os.getenv('RECEPTIVITI_SECRET', ''), url=os.getenv('RECEPTIVITI_URL', ''), version=os.getenv('RECEPTIVITI_VERSION', ''), endpoint=os.getenv('RECEPTIVITI_ENDPOINT', ''))

Send texts to be scored by the API.

Parameters:

Name	Type	Description	Default
text	str | list[str] | DataFrame	Text to be processed, as a string or vector of strings containing the text itself, or the path to a file from which to read in text. If a DataFrame, text_column is used to extract such a vector. A string may also represent a directory in which to search for files. To best ensure paths are not treated as texts, either set text_as_path to True, or use directory to enter a directory path, or files to enter a vector of file paths.	None
output	str	Path to a file to write results to.	None
ids	str | list[str | int]	Vector of IDs for each text, or a column name in text containing IDs.	None
text_column	str	Column name in text containing text.	None
id_column	str	Column name in text containing IDs.	None
files	list[str]	Vector of file paths, as alternate entry to text.	None
directory	str	A directory path to search for files in, as alternate entry to text.	None
file_type	str	Extension of the file(s) to be read in from a directory (txt or csv).	'txt'
encoding	str | None	Encoding of file(s) to be read in; one of the standard encodings. If this is None (default), encoding will be predicted for each file, but this can potentially fail, resulting in mis-encoded characters. For best (and fastest) results, specify encoding.	None
return_text	bool	If True, will include a text column in the output with the original text.	False
api_args	dict	Additional arguments to include in the request.	None
frameworks	str | list	One or more names of frameworks to return.	None
framework_prefix	bool	If False, will drop framework prefix from column names. If one framework is selected, will default to False.	None
bundle_size	int	Maximum number of texts per bundle.	1000
bundle_byte_limit	float	Maximum byte size of each bundle.	7500000.0
collapse_lines	bool	If True, will treat files as containing single texts, and collapse multiple lines.	False
retry_limit	int	Number of times to retry a failed request.	50
clear_cache	bool	If True, will delete the cache before processing.	False
request_cache	bool	If False, will not temporarily save raw requests for reuse within a day.	True
cores	int	Number of CPU cores to use when processing multiple bundles.	1
in_memory	bool | None	If False, will write bundles to disc, to be loaded when processed. Defaults to True when processing in parallel.	None
verbose	bool	If True, will print status messages and preserve the progress bar.	False
progress_bar	str | bool	If False, will not display a progress bar.	getenv('RECEPTIVITI_PB', 'True')
overwrite	bool	If True, will overwrite an existing output file.	False
text_as_paths	bool	If True, will explicitly mark text as a list of file paths. Otherwise, this will be detected.	False
dotenv	bool | str	Path to a .env file to read environment variables from. By default, will for a file in the current directory or ~/Documents. Passed to readin_env as path.	True
cache	bool | str	Path to a cache directory, or True to use the default directory.	getenv('RECEPTIVITI_CACHE', '')
cache_overwrite	bool	If True, will not check the cache for previously cached texts, but will store results in the cache (unlike cache = False).	False
cache_format	str	File format of the cache, of available Arrow formats.	getenv('RECEPTIVITI_CACHE_FORMAT', '')
key	str	Your API key.	getenv('RECEPTIVITI_KEY', '')
secret	str	Your API secret.	getenv('RECEPTIVITI_SECRET', '')
url	str	The URL of the API; defaults to https://api.receptiviti.com.	getenv('RECEPTIVITI_URL', '')
version	str	Version of the API; defaults to v1.	getenv('RECEPTIVITI_VERSION', '')
endpoint	str	Endpoint of the API; defaults to framework.	getenv('RECEPTIVITI_ENDPOINT', '')

Returns:

Type	Description
DataFrame	Scores associated with each input text.

Cache

If cache is specified, results for unique texts are saved in an Arrow database in the cache location (os.getenv("RECEPTIVITI_CACHE")), and are retrieved with subsequent requests. This ensures that the exact same texts are not re-sent to the API. This does, however, add some processing time and disc space usage.

If cache if True, a default directory (receptiviti_cache) will be looked for in the system's temporary directory (tempfile.gettempdir()).

The primary cache is checked when each bundle is processed, and existing results are loaded at that time. When processing many bundles in parallel, and many results have been cached, this can cause the system to freeze and potentially crash. To avoid this, limit the number of cores, or disable parallel processing.

The cache_format arguments (or the RECEPTIVITI_CACHE_FORMAT environment variable) can be used to adjust the format of the cache.

You can use the cache independently with pyarrow.dataset.dataset(os.getenv("RECEPTIVITI_CACHE")).

You can also set the clear_cache argument to True to clear the cache before it is used again, which may be useful if the cache has gotten big, or you know new results will be returned.

Even if a cached result exists, it will be reprocessed if it does not have all of the variables of new results, but this depends on there being at least 1 uncached result. If, for instance, you add a framework to your account and want to reprocess a previously processed set of texts, you would need to first clear the cache.

Either way, duplicated texts within the same call will only be sent once.

The request_cache argument controls a more temporary cache of each bundle request. This is cleared after a day. You might want to set this to False if a new framework becomes available on your account and you want to process a set of text you re-processed recently.

Another temporary cache is made when in_memory is False, which is the default when processing in parallel (when there is more than 1 bundle and cores is over 1). This is a temporary directory that contains a file for each unique bundle, which is read in as needed by the parallel workers.

Parallelization

texts are split into bundles based on the bundle_size argument. Each bundle represents a single request to the API, which is why they are limited to 1000 texts and a total size of 10 MB. When there is more than one bundle and cores is greater than 1, bundles are processed by multiple cores.

If you have texts spread across multiple files, they can be most efficiently processed in parallel if each file contains a single text (potentially collapsed from multiple lines). If files contain multiple texts (i.e., collapse_lines=False), then texts need to be read in before bundling in order to ensure bundles are under the length limit.

If you are calling this function from a script, parallelization will involve rerunning that script in each process, so anything you don't want rerun should be protected by a check that __name__ equals "__main__" (placed within an if __name__ == "__main__": clause).

Source code in src\receptiviti\request.py

def request(
    text: Union[str, List[str], pandas.DataFrame, None] = None,
    output: Union[str, None] = None,
    ids: Union[str, List[str], List[int], None] = None,
    text_column: Union[str, None] = None,
    id_column: Union[str, None] = None,
    files: Union[List[str], None] = None,
    directory: Union[str, None] = None,
    file_type: str = "txt",
    encoding: Union[str, None] = None,
    return_text=False,
    api_args: Union[dict, None] = None,
    frameworks: Union[str, List[str], None] = None,
    framework_prefix: Union[bool, None] = None,
    bundle_size=1000,
    bundle_byte_limit=75e5,
    collapse_lines=False,
    retry_limit=50,
    clear_cache=False,
    request_cache=True,
    cores=1,
    in_memory: Union[bool, None] = None,
    verbose=False,
    progress_bar: Union[str, bool] = os.getenv("RECEPTIVITI_PB", "True"),
    overwrite=False,
    make_request=True,
    text_as_paths=False,
    dotenv: Union[bool, str] = True,
    cache: Union[str, bool] = os.getenv("RECEPTIVITI_CACHE", ""),
    cache_overwrite=False,
    cache_format=os.getenv("RECEPTIVITI_CACHE_FORMAT", ""),
    key=os.getenv("RECEPTIVITI_KEY", ""),
    secret=os.getenv("RECEPTIVITI_SECRET", ""),
    url=os.getenv("RECEPTIVITI_URL", ""),
    version=os.getenv("RECEPTIVITI_VERSION", ""),
    endpoint=os.getenv("RECEPTIVITI_ENDPOINT", ""),
) -> pandas.DataFrame:
    """
    Send texts to be scored by the API.

    Args:
        text (str | list[str] | pandas.DataFrame): Text to be processed, as a string or vector of
            strings containing the text itself, or the path to a file from which to read in text.
            If a DataFrame, `text_column` is used to extract such a vector. A string may also
            represent a directory in which to search for files. To best ensure paths are not
            treated as texts, either set `text_as_path` to `True`, or use `directory` to enter
            a directory path, or `files` to enter a vector of file paths.
        output (str): Path to a file to write results to.
        ids (str | list[str | int]): Vector of IDs for each `text`, or a column name in `text`
            containing IDs.
        text_column (str): Column name in `text` containing text.
        id_column (str): Column name in `text` containing IDs.
        files (list[str]): Vector of file paths, as alternate entry to `text`.
        directory (str): A directory path to search for files in, as alternate entry to `text`.
        file_type (str): Extension of the file(s) to be read in from a directory (`txt` or `csv`).
        encoding (str | None): Encoding of file(s) to be read in; one of the
            [standard encodings](https://docs.python.org/3/library/codecs.html#standard-encodings).
            If this is `None` (default), encoding will be predicted for each file, but this can
            potentially fail, resulting in mis-encoded characters. For best (and fastest) results,
            specify encoding.
        return_text (bool): If `True`, will include a `text` column in the output with the
            original text.
        api_args (dict): Additional arguments to include in the request.
        frameworks (str | list): One or more names of frameworks to return.
        framework_prefix (bool): If `False`, will drop framework prefix from column names.
            If one framework is selected, will default to `False`.
        bundle_size (int): Maximum number of texts per bundle.
        bundle_byte_limit (float): Maximum byte size of each bundle.
        collapse_lines (bool): If `True`, will treat files as containing single texts, and
            collapse multiple lines.
        retry_limit (int): Number of times to retry a failed request.
        clear_cache (bool): If `True`, will delete the `cache` before processing.
        request_cache (bool): If `False`, will not temporarily save raw requests for reuse
            within a day.
        cores (int): Number of CPU cores to use when processing multiple bundles.
        in_memory (bool | None): If `False`, will write bundles to disc, to be loaded when
            processed. Defaults to `True` when processing in parallel.
        verbose (bool): If `True`, will print status messages and preserve the progress bar.
        progress_bar (str | bool): If `False`, will not display a progress bar.
        overwrite (bool): If `True`, will overwrite an existing `output` file.
        text_as_paths (bool): If `True`, will explicitly mark `text` as a list of file paths.
            Otherwise, this will be detected.
        dotenv (bool | str): Path to a .env file to read environment variables from. By default,
            will for a file in the current directory or `~/Documents`.
            Passed to `readin_env` as `path`.
        cache (bool | str): Path to a cache directory, or `True` to use the default directory.
        cache_overwrite (bool): If `True`, will not check the cache for previously cached texts,
            but will store results in the cache (unlike `cache = False`).
        cache_format (str): File format of the cache, of available Arrow formats.
        key (str): Your API key.
        secret (str): Your API secret.
        url (str): The URL of the API; defaults to `https://api.receptiviti.com`.
        version (str): Version of the API; defaults to `v1`.
        endpoint (str): Endpoint of the API; defaults to `framework`.

    Returns:
        Scores associated with each input text.

    Cache:
        If `cache` is specified, results for unique texts are saved in an Arrow database
        in the cache location (`os.getenv("RECEPTIVITI_CACHE")`), and are retrieved with
        subsequent requests. This ensures that the exact same texts are not re-sent to the API.
        This does, however, add some processing time and disc space usage.

        If `cache` if `True`, a default directory (`receptiviti_cache`) will be
        looked for in the system's temporary directory (`tempfile.gettempdir()`).

        The primary cache is checked when each bundle is processed, and existing results are
        loaded at that time. When processing many bundles in parallel, and many results have
        been cached, this can cause the system to freeze and potentially crash.
        To avoid this, limit the number of cores, or disable parallel processing.

        The `cache_format` arguments (or the `RECEPTIVITI_CACHE_FORMAT` environment variable) can be
        used to adjust the format of the cache.

        You can use the cache independently with
        `pyarrow.dataset.dataset(os.getenv("RECEPTIVITI_CACHE"))`.

        You can also set the `clear_cache` argument to `True` to clear the cache before it is used
        again, which may be useful if the cache has gotten big, or you know new results will be
        returned.

        Even if a cached result exists, it will be reprocessed if it does not have all of the
        variables of new results, but this depends on there being at least 1 uncached result. If,
        for instance, you add a framework to your account and want to reprocess a previously
        processed set of texts, you would need to first clear the cache.

        Either way, duplicated texts within the same call will only be sent once.

        The `request_cache` argument controls a more temporary cache of each bundle request. This
        is cleared after a day. You might want to set this to `False` if a new framework becomes
        available on your account and you want to process a set of text you re-processed recently.

        Another temporary cache is made when `in_memory` is `False`, which is the default when
        processing in parallel (when there is more than 1 bundle and `cores` is over 1). This is a
        temporary directory that contains a file for each unique bundle, which is read in as needed
        by the parallel workers.

    Parallelization:
        `text`s are split into bundles based on the `bundle_size` argument. Each bundle represents
        a single request to the API, which is why they are limited to 1000 texts and a total size
        of 10 MB. When there is more than one bundle and `cores` is greater than 1, bundles are
        processed by multiple cores.

        If you have texts spread across multiple files, they can be most efficiently processed in
        parallel if each file contains a single text (potentially collapsed from multiple lines).
        If files contain multiple texts (i.e., `collapse_lines=False`), then texts need to be
        read in before bundling in order to ensure bundles are under the length limit.

        If you are calling this function from a script, parallelization will involve rerunning
        that script in each process, so anything you don't want rerun should be protected by
        a check that `__name__` equals `"__main__"`
        (placed within an `if __name__ == "__main__":` clause).
    """
    if cores > 1 and current_process().name != "MainProcess":
        return
    if output is not None and os.path.isfile(output) and not overwrite:
        msg = "`output` file already exists; use `overwrite=True` to overwrite it"
        raise RuntimeError(msg)
    start_time = perf_counter()

    if request_cache:
        if verbose:
            print(f"preparing request cache ({perf_counter() - start_time:.4f})")
        _manage_request_cache()

    # resolve credentials and check status
    if dotenv:
        readin_env("." if isinstance(dotenv, bool) else dotenv)
    if not url:
        url = os.getenv("RECEPTIVITI_URL", "https://api.receptiviti.com")
    url_parts = re.search("/([Vv]\\d+)/?([^/]+)?", url)
    if url_parts:
        from_url = url_parts.groups()
        if not version and from_url[0] is not None:
            version = from_url[0]
        if not endpoint and from_url[1] is not None:
            endpoint = from_url[1]
    url = ("https://" if re.match("http", url, re.I) is None else "") + re.sub(
        "/+[Vv]\\d+(?:/.*)?$|/+$", "", url
    )
    if not key:
        key = os.getenv("RECEPTIVITI_KEY", "")
    if not secret:
        secret = os.getenv("RECEPTIVITI_SECRET", "")
    if not version:
        version = os.getenv("RECEPTIVITI_VERSION", "v1")
    if not endpoint:
        endpoint_default = "framework" if version.lower() == "v1" else "taxonomies"
        endpoint = os.getenv("RECEPTIVITI_ENDPOINT", endpoint_default)
    api_status = status(url, key, secret, dotenv, verbose=False)
    if not api_status or api_status.status_code != 200:
        msg = (
            f"API status failed: {api_status.status_code}: {api_status.reason}"
            if api_status
            else "URL is not reachable"
        )
        raise RuntimeError(msg)

    # resolve text and ids
    text_as_dir = False
    if text is None:
        if directory is not None:
            text = directory
            text_as_dir = True
        elif files is not None:
            text_as_paths = True
            text = files
        else:
            msg = "enter text as the first argument, or use the `files` or `directory` arguments"
            raise RuntimeError(msg)
    if isinstance(text, str) and (text_as_dir or text_as_paths or len(text) < 260):
        if not text_as_dir and os.path.isfile(text):
            if verbose:
                print(f"reading in texts from a file ({perf_counter() - start_time:.4f})")
            text = _readin([text], text_column, id_column, collapse_lines, encoding)
            if isinstance(text, pandas.DataFrame):
                id_column = "ids"
                text_column = "text"
            text_as_paths = False
        elif os.path.isdir(text):
            text = glob(f"{text}/*{file_type}")
            text_as_paths = True
        elif os.path.isdir(os.path.dirname(text)):
            msg = f"`text` appears to point to a directory, but it does not exist: {text}"
            raise RuntimeError(msg)
    if isinstance(text, pandas.DataFrame):
        if id_column is not None:
            if id_column in text:
                ids = text[id_column].to_list()
            else:
                msg = f"`id_column` ({id_column}) is not in `text`"
                raise IndexError(msg)
        if text_column is not None:
            if text_column in text:
                text = text[text_column].to_list()
            else:
                msg = f"`text_column` ({text_column}) is not in `text`"
                raise IndexError(msg)
        else:
            msg = "`text` is a DataFrame, but no `text_column` is specified"
            raise RuntimeError(msg)
    if isinstance(text, str):
        text = [text]
    text_is_path = all(
        isinstance(t, str) and (text_as_paths or len(t) < 260) and os.path.isfile(t) for t in text
    )
    if text_as_paths and not text_is_path:
        msg = "`text` treated as a list of files, but not all of the entries exist"
        raise RuntimeError(msg)
    if text_is_path and not collapse_lines:
        ids = text
        text = _readin(text, text_column, id_column, collapse_lines, encoding)
        if isinstance(text, pandas.DataFrame):
            if id_column is None:
                ids = text["ids"].to_list()
            elif id_column in text:
                ids = text[id_column].to_list()
            if text_column is None:
                text_column = "text"
            text = text[text_column].to_list()
        text_is_path = False
    if ids is None and text_is_path:
        ids = text

    id_specified = ids is not None
    if ids is None:
        ids = numpy.arange(1, len(text) + 1).tolist()
    elif len(ids) != len(text):
        msg = "`ids` is not the same length as `text`"
        raise RuntimeError(msg)
    original_ids = set(ids)
    if len(ids) != len(original_ids):
        msg = "`ids` contains duplicates"
        raise RuntimeError(msg)

    # prepare bundles
    if verbose:
        print(f"preparing text ({perf_counter() - start_time:.4f})")
    data = pandas.DataFrame({"text": text, "id": ids})
    n_original = len(data)
    data_subset = data[
        ~(data.duplicated(subset=["text"]) | (data["text"] == "") | data["text"].isna())
    ]
    n_texts = len(data_subset)
    if not n_texts:
        msg = "no valid texts to process"
        raise RuntimeError(msg)
    bundle_size = max(1, bundle_size)
    n_bundles = math.ceil(n_texts / min(1000, bundle_size))
    groups = data_subset.groupby(
        numpy.sort(numpy.tile(numpy.arange(n_bundles) + 1, bundle_size))[:n_texts],
        group_keys=False,
    )
    bundles = []
    for _, group in groups:
        if sys.getsizeof(group) > bundle_byte_limit:
            start = current = end = 0
            for txt in group["text"]:
                size = os.stat(txt).st_size if text_is_path else sys.getsizeof(txt)
                if size > bundle_byte_limit:
                    msg = (
                        "one of your texts is over the bundle size"
                        f" limit ({bundle_byte_limit / 1e6} MB)"
                    )
                    raise RuntimeError(msg)
                if (current + size) > bundle_byte_limit:
                    bundles.append(group[start:end])
                    start = end = end + 1
                    current = size
                else:
                    end += 1
                    current += size
            bundles.append(group[start:])
        else:
            bundles.append(group)
    n_bundles = len(bundles)
    if verbose:
        print(
            f"prepared {n_texts} unique text{'s' if n_texts > 1 else ''} in "
            f"{n_bundles} {'bundles' if n_bundles > 1 else 'bundle'}",
            f"({perf_counter() - start_time:.4f})",
        )

    # process bundles
    if isinstance(cache, str):
        if cache:
            if clear_cache and os.path.exists(cache):
                shutil.rmtree(cache, True)
            os.makedirs(cache, exist_ok=True)
            if not cache_format:
                cache_format = os.getenv("RECEPTIVITI_CACHE_FORMAT", "parquet")
        else:
            cache = False
    opts = {
        "url": f"{url}/{version}/{endpoint}/bulk".lower(),
        "auth": requests.auth.HTTPBasicAuth(key, secret),
        "retries": retry_limit,
        "add": {} if api_args is None else api_args,
        "request_cache": request_cache,
        "cache": "" if cache_overwrite or isinstance(cache, bool) and not cache else cache,
        "cache_format": cache_format,
        "make_request": make_request,
        "text_is_path": text_is_path,
        "text_column": text_column,
        "id_column": id_column,
        "collapse_lines": collapse_lines,
        "encoding": encoding,
    }
    opts["add_hash"] = hashlib.md5(
        json.dumps(
            {**opts["add"], "url": opts["url"], "key": key, "secret": secret},
            separators=(",", ":"),
        ).encode()
    ).hexdigest()
    if isinstance(progress_bar, str):
        progress_bar = progress_bar == "True"
    use_pb = (verbose and progress_bar) or progress_bar
    parallel = n_bundles > 1 and cores > 1
    if in_memory is None:
        in_memory = not parallel
    with TemporaryDirectory() as scratch_cache:
        if not in_memory:
            if verbose:
                print(f"writing to scratch cache ({perf_counter() - start_time:.4f})")

            def write_to_scratch(i: int, bundle: pandas.DataFrame):
                temp = f"{scratch_cache}/{i}.json"
                with open(temp, "wb") as scratch:
                    pickle.dump(bundle, scratch)
                return temp

            bundles = [write_to_scratch(i, b) for i, b in enumerate(bundles)]
        if parallel:
            if verbose:
                print(f"requesting in parallel ({perf_counter() - start_time:.4f})")
            waiter: "Queue[pandas.DataFrame]" = Queue()
            queue: "Queue[tuple[int, pandas.DataFrame]]" = Queue()
            manager = Process(
                target=_queue_manager,
                args=(queue, waiter, n_texts, n_bundles, use_pb, verbose),
            )
            manager.start()
            nb = math.ceil(n_bundles / min(n_bundles, cores))
            cores = math.ceil(n_bundles / nb)
            procs = [
                Process(
                    target=_process,
                    args=(bundles[(i * nb) : min(n_bundles, (i + 1) * nb)], opts, queue),
                )
                for i in range(cores)
            ]
            for cl in procs:
                cl.start()
            res = waiter.get()
        else:
            if verbose:
                print(f"requesting serially ({perf_counter() - start_time:.4f})")
            pb = tqdm(total=n_texts, leave=verbose) if use_pb else None
            res = _process(bundles, opts, pb=pb)
            if pb is not None:
                pb.close()
    if verbose:
        print(f"done requesting ({perf_counter() - start_time:.4f})")

    # finalize
    if not res.shape[0]:
        msg = "no results"
        raise RuntimeError(msg)
    if isinstance(cache, str):
        _update_cache(res, cache, cache_format, verbose, start_time, [e[0] for e in opts["add"]])
    if verbose:
        print(f"preparing output ({perf_counter() - start_time:.4f})")
    data.set_index("id", inplace=True)
    res.set_index("id", inplace=True)
    if len(res) != n_original:
        res = res.join(data["text"])
        data_absent = data.loc[list(set(data.index).difference(res.index))]
        data_absent = data_absent.loc[data_absent["text"].isin(res["text"])]
        if data.size:
            res = res.reset_index()
            res.set_index("text", inplace=True)
            data_dupes = res.loc[data_absent["text"]]
            data_dupes["id"] = data_absent.index.to_list()
            res = pandas.concat([res, data_dupes])
            res.reset_index(inplace=True, drop=True)
            res.set_index("id", inplace=True)
    res = res.join(data["text"], how="right")
    if not return_text:
        res.drop("text", axis=1, inplace=True)
    res = res.reset_index()

    if output is not None:
        if verbose:
            print(f"writing results to file: {output} ({perf_counter() - start_time:.4f})")
        res.to_csv(output, index=False)

    drops = ["custom", "bin"]
    if not id_specified:
        drops.append("id")
    res.drop(
        list({*drops}.intersection(res.columns)),
        axis="columns",
        inplace=True,
    )
    if frameworks is not None:
        if verbose:
            print(f"selecting frameworks ({perf_counter() - start_time:.4f})")
        if isinstance(frameworks, str):
            frameworks = [frameworks]
        if len(frameworks) == 1 and framework_prefix is None:
            framework_prefix = False
        select = []
        if id_specified:
            select.append("id")
        if return_text:
            select.append("text")
        select.append("text_hash")
        res = res.filter(regex=f"^(?:{'|'.join(select + frameworks)})(?:$|\\.)")
    if isinstance(framework_prefix, bool) and not framework_prefix:
        prefix_pattern = re.compile("^[^.]+\\.")
        res.columns = pandas.Index([prefix_pattern.sub("", col) for col in res.columns])

    if verbose:
        print(f"done ({perf_counter() - start_time:.4f})")

    return res