run_data_measurements.py

import argparse
import json
import textwrap
from os import mkdir
from os.path import join as pjoin, isdir

from data_measurements import dataset_statistics
from data_measurements import dataset_utils



def load_or_prepare_widgets(ds_args, show_embeddings=False, use_cache=False):
    """
    Loader specifically for the widgets used in the app -- does not compute
    intermediate files, unless they are not there and are needed for a file
    used in the UI.
    Does not take specifications from user; does all widgets.
    Args:
        ds_args: Dataset configuration settings (config name, split, etc)
        show_embeddings: Whether to compute embeddings (slow)
        use_cache: Whether to grab files that have already been computed

    Returns:
        Saves files to disk in cache_dir, if user has not specified another dir.
    """

    if not isdir(ds_args["cache_dir"]):
        print("Creating cache")
        # We need to preprocess everything.
        # This should eventually all go into a prepare_dataset CLI
        mkdir(ds_args["cache_dir"])


    dstats = dataset_statistics.DatasetStatisticsCacheClass(**ds_args,
                                                            use_cache=use_cache)
    # Embeddings widget
    dstats.load_or_prepare_dataset()
    # Header widget
    dstats.load_or_prepare_dset_peek()
    # General stats widget
    dstats.load_or_prepare_general_stats()
    # Labels widget
    try:
        dstats.set_label_field(ds_args['label_field'])
        dstats.load_or_prepare_labels()
    except:
        pass
    # Text lengths widget
    dstats.load_or_prepare_text_lengths()
    if show_embeddings:
        # Embeddings widget
        dstats.load_or_prepare_embeddings()
    # Text duplicates widget
    dstats.load_or_prepare_text_duplicates()
    # nPMI widget
    dstats.load_or_prepare_npmi()
    npmi_stats = dstats.npmi_stats
    # Handling for all pairs; in the UI, people select.
    do_npmi(npmi_stats)
    # Zipf widget
    dstats.load_or_prepare_zipf()


def load_or_prepare(dataset_args, use_cache=False):
    """
    Users can specify which aspects of the dataset they would like to compute.
    This additionally computes intermediate files not used in the UI.
    If the calculation flag is not specified by the user (-w), calculates all
    except for embeddings, as those are quite time consuming so should be
    specified separately.
    Args:
        dataset_args: Dataset configuration settings (config name, split, etc)
        use_cache: Whether to grab files that have already been computed

    Returns:
        Saves files to disk in cache_dir, if user has not specified another dir.
    """
    all = False
    dstats = dataset_statistics.DatasetStatisticsCacheClass(**dataset_args,
                                                            use_cache=use_cache)
    print("Loading dataset.")
    dstats.load_or_prepare_dataset()
    print("Dataset loaded.  Preparing vocab.")
    dstats.load_or_prepare_vocab()
    print("Vocab prepared.")

    if not dataset_args["calculation"]:
        all = True

    if all or dataset_args["calculation"] == "general":
        print("\n* Calculating general statistics.")
        dstats.load_or_prepare_general_stats()
        print("Done!")
        print("Basic text statistics now available at %s." %
              dstats.general_stats_json_fid)
        print(
            "Text duplicates now available at %s." % dstats.dup_counts_df_fid
        )

    if all or dataset_args["calculation"] == "lengths":
        print("\n* Calculating text lengths.")
        dstats.load_or_prepare_text_lengths()
        print("Done!")

    if all or dataset_args["calculation"] == "labels":
        if not dstats.label_field:
            print("Warning: You asked for label calculation, but didn't "
                  "provide the labels field name.  Assuming it is 'label'...")
            dstats.set_label_field("label")
        else:
            print("\n* Calculating label distribution.")
            dstats.load_or_prepare_labels()
            fig_label_html = pjoin(dstats.cache_path, "labels_fig.html")
            fig_label_json = pjoin(dstats.cache_path, "labels.json")
            dstats.fig_labels.write_html(fig_label_html)
            with open(fig_label_json, "w+") as f:
                json.dump(dstats.fig_labels.to_json(), f)
            print("Done!")
            print("Label distribution now available at %s." %
                  dstats.label_dset_fid)
            print("Figure saved to %s." % fig_label_html)

    if all or dataset_args["calculation"] == "npmi":
        print("\n* Preparing nPMI.")
        npmi_stats = dataset_statistics.nPMIStatisticsCacheClass(
            dstats, use_cache=use_cache
        )
        do_npmi(npmi_stats)
        print("Done!")
        print(
            "nPMI results now available in %s for all identity terms that "
            "occur more than 10 times and all words that "
            "co-occur with both terms."
            % npmi_stats.pmi_cache_path
        )

    if all or dataset_args["calculation"] == "zipf":
        print("\n* Preparing Zipf.")
        zipf_fig_fid = pjoin(dstats.cache_path, "zipf_fig.html")
        zipf_json_fid = pjoin(dstats.cache_path, "zipf_fig.json")
        dstats.load_or_prepare_zipf()
        zipf_fig = dstats.zipf_fig
        with open(zipf_json_fid, "w+") as f:
            json.dump(zipf_fig.to_json(), f)
        zipf_fig.write_html(zipf_fig_fid)
        print("Done!")
        print("Zipf results now available at %s." % dstats.zipf_fid)
        print(
            "Figure saved to %s, with corresponding json at %s."
            % (zipf_fig_fid, zipf_json_fid)
        )

    # Don't do this one until someone specifically asks for it -- takes awhile.
    if dataset_args["calculation"] == "embeddings":
        print("\n* Preparing text embeddings.")
        dstats.load_or_prepare_embeddings()


def do_npmi(npmi_stats):
    available_terms = npmi_stats.load_or_prepare_npmi_terms()
    completed_pairs = {}
    print("Iterating through terms for joint npmi.")
    for term1 in available_terms:
        for term2 in available_terms:
            if term1 != term2:
                sorted_terms = tuple(sorted([term1, term2]))
                if sorted_terms not in completed_pairs:
                    term1, term2 = sorted_terms
                    print("Computing nPMI statistics for %s and %s" % (term1, term2))
                    _ = npmi_stats.load_or_prepare_joint_npmi(sorted_terms)
                    completed_pairs[tuple(sorted_terms)] = {}


def get_text_label_df(
    ds_name,
    config_name,
    split_name,
    text_field,
    label_field,
    calculation,
    out_dir,
    use_cache=True,
):
    if not use_cache:
        print("Not using any cache; starting afresh")
    ds_name_to_dict = dataset_utils.get_dataset_info_dicts(ds_name)
    if label_field:
        label_field, label_names = (
            ds_name_to_dict[ds_name][config_name]["features"][label_field][0]
            if len(ds_name_to_dict[ds_name][config_name]["features"][label_field]) > 0
            else ((), [])
        )
    else:
        label_field = ()
        label_names = []
    dataset_args = {
        "dset_name": ds_name,
        "dset_config": config_name,
        "split_name": split_name,
        "text_field": text_field,
        "label_field": label_field,
        "label_names": label_names,
        "calculation": calculation,
        "cache_dir": out_dir,
    }
    load_or_prepare(dataset_args, use_cache=use_cache)


def main():
    # TODO: Make this the Hugging Face arg parser
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawDescriptionHelpFormatter,
        description=textwrap.dedent(
            """

         Example for hate speech18 dataset:
         python3 run_data_measurements.py --dataset="hate_speech18" --config="default" --split="train" --feature="text"

         Example for IMDB dataset:
         python3 run_data_measurements.py --dataset="imdb" --config="plain_text" --split="train" --label_field="label" --feature="text"
         """
        ),
    )

    parser.add_argument(
        "-d", "--dataset", required=True, help="Name of dataset to prepare"
    )
    parser.add_argument(
        "-c", "--config", required=True, help="Dataset configuration to prepare"
    )
    parser.add_argument(
        "-s", "--split", required=True, type=str, help="Dataset split to prepare"
    )
    parser.add_argument(
        "-f",
        "--feature",
        required=True,
        type=str,
        default="text",
        help="Text column to prepare",
    )
    parser.add_argument(
        "-w",
        "--calculation",
        help="""What to calculate (defaults to everything except embeddings).\n
                                                    Options are:\n

                                                    - `general` (for duplicate counts, missing values, length statistics.)\n

                                                    - `lengths` for text length distribution\n

                                                    - `labels` for label distribution\n

                                                    - `embeddings` (Warning: Slow.)\n

                                                    - `npmi` for word associations\n

                                                    - `zipf` for zipfian statistics
                                                    """,
    )
    parser.add_argument(
        "-l",
        "--label_field",
        type=str,
        required=False,
        default="",
        help="Field name for label column in dataset (Required if there is a label field that you want information about)",
    )
    parser.add_argument(
        "--cached",
        default=False,
        required=False,
        action="store_true",
        help="Whether to use cached files (Optional)",
    )
    parser.add_argument(
        "--do_html",
        default=False,
        required=False,
        action="store_true",
        help="Whether to write out corresponding HTML files (Optional)",
    )
    parser.add_argument("--out_dir", default="cache_dir", help="Where to write out to.")

    args = parser.parse_args()
    print("Proceeding with the following arguments:")
    print(args)
    # run_data_measurements.py -d hate_speech18 -c default -s train -f text -w npmi
    get_text_label_df(args.dataset, args.config, args.split, args.feature,
                      args.label_field, args.calculation, args.out_dir,
                      use_cache=args.cached)
    print()


if __name__ == "__main__":
    main()