readers.py

# License: BSD 3 clause
"""
Handles loading data from various types of data files.

:author: Dan Blanchard (dblanchard@ets.org)
:author: Michael Heilman (mheilman@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)
:organization: ETS
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import csv
import json
import logging
import re
import sys
from csv import DictReader
from itertools import chain, islice
from io import open, BytesIO, StringIO

import numpy as np
from bs4 import UnicodeDammit
from six import iteritems, PY2, PY3, string_types, text_type
from six.moves import zip
from sklearn.feature_extraction import FeatureHasher

from skll.data import FeatureSet
from skll.data.dict_vectorizer import DictVectorizer


class Reader(object):

    """
    A helper class to make picklable iterators out of example
    dictionary generators.

    Parameters
    ----------
    path_or_list : str or list of dict
        Path or a list of example dictionaries.
    quiet : bool, optional
        Do not print "Loading..." status message to stderr.
        Defaults to ``True``.
    ids_to_floats : bool, optional
        Convert IDs to float to save memory. Will raise error
        if we encounter an a non-numeric ID.
        Defaults to ``False``.
    label_col : str, optional
        Name of the column which contains the class labels
        for ARFF/CSV/TSV files. If no column with that name
        exists, or ``None`` is specified, the data is
        considered to be unlabelled.
        Defaults to ``'y'``.
    id_col : str, optional
        Name of the column which contains the instance IDs.
        If no column with that name exists, or ``None`` is
        specified, example IDs will be automatically generated.
        Defaults to ``'id'``.
    class_map : dict, optional
        Mapping from original class labels to new ones. This is
        mainly used for collapsing multiple labels into a single
        class. Anything not in the mapping will be kept the same.
        Defaults to ``None``.
    sparse : bool, optional
        Whether or not to store the features in a numpy CSR
        matrix when using a DictVectorizer to vectorize the
        features.
        Defaults to ``True``.
    feature_hasher : bool, optional
        Whether or not a FeatureHasher should be used to
        vectorize the features.
        Defaults to ``False``.
    num_features : int, optional
        If using a FeatureHasher, how many features should the
        resulting matrix have?  You should set this to a power
        of 2 greater than the actual number of features to
        avoid collisions.
        Defaults to ``None``.
    logger : logging.Logger, optional
        A logger instance to use to log messages instead of creating
        a new one by default.
        Defaults to ``None``.
    """

    def __init__(self, path_or_list, quiet=True, ids_to_floats=False,
                 label_col='y', id_col='id', class_map=None, sparse=True,
                 feature_hasher=False, num_features=None, logger=None):
        super(Reader, self).__init__()
        self.path_or_list = path_or_list
        self.quiet = quiet
        self.ids_to_floats = ids_to_floats
        self.label_col = label_col
        self.id_col = id_col
        self.class_map = class_map
        self._progress_msg = ''
        if feature_hasher:
            self.vectorizer = FeatureHasher(n_features=num_features)
        else:
            self.vectorizer = DictVectorizer(sparse=sparse)
        self.logger = logger if logger else logging.getLogger(__name__)

    @classmethod
    def for_path(cls, path_or_list, **kwargs):
        """
        Instantiate the appropriate Reader sub-class based on the
        file extension of the given path. Or use a dictionary reader
        if the input is a list of dictionaries.

        Parameters
        ----------
        path_or_list : str or list of dicts
            A path or list of example dictionaries.
        kwargs : dict, optional
            The arguments to the Reader object being instantiated.

        Returns
        -------
        reader : skll.Reader
            A new instance of the Reader sub-class that is
            appropriate for the given path.

        Raises
        ------
        ValueError
            If file does not have a valid extension.
        """
        if not isinstance(path_or_list, string_types):
            return DictListReader(path_or_list)
        else:
            # Get lowercase extension for file extension checking
            ext = '.' + path_or_list.rsplit('.', 1)[-1].lower()
            if ext not in EXT_TO_READER:
                raise ValueError(('Example files must be in either .arff, '
                                  '.csv, .jsonlines, .megam, .ndj, or .tsv '
                                  'format. You specified: '
                                  '{}').format(path_or_list))
        return EXT_TO_READER[ext](path_or_list, **kwargs)

    def _sub_read(self, f):
        """
        Does the actual reading of the given file or list.

        Parameters
        ----------
        f : file buffer
            An open file to iterate through.

        Raises
        ------
        NotImplementedError
        """
        raise NotImplementedError

    def _print_progress(self, progress_num, end="\r"):
        """
        Helper method to print out progress numbers in proper format.
        Nothing gets printed if ``self.quiet`` is ``True``.

        Parameters
        ----------
        progress_num
            Progress indicator value. Usually either a line
            number or a percentage. Must be able to convert to string.

        end : str, optional
            The string to put at the end of the line.  "\\r" should be
            used for every update except for the final one.
            Defaults to ``'\r'``.
        """
        # Print out status
        if not self.quiet:
            print("{}{:>15}".format(self._progress_msg, progress_num),
                  end=end, file=sys.stderr)
            sys.stderr.flush()

    def read(self):
        """
        Loads examples in the `.arff`, `.csv`, `.jsonlines`, `.libsvm`,
        `.megam`, `.ndj`, or `.tsv` formats.

        Returns
        -------
        feature_set : skll.FeatureSet
            ``FeatureSet`` instance representing the input file.

        Raises
        ------
        ValueError
            If ``ids_to_floats`` is True, but IDs cannot be converted.
        ValueError
            If no features are found.
        ValueError
            If the example IDs are not unique.
        """
        self.logger.debug('Path: %s', self.path_or_list)

        if not self.quiet:
            self._progress_msg = "Loading {}...".format(self.path_or_list)
            print(self._progress_msg, end="\r", file=sys.stderr)
            sys.stderr.flush()

        # Get labels and IDs
        ids = []
        labels = []
        ex_num = 0
        with open(self.path_or_list, 'r' if PY3 else 'rb') as f:
            for ex_num, (id_, class_, _) in enumerate(self._sub_read(f), start=1):
                # Update lists of IDs, clases, and features
                if self.ids_to_floats:
                    try:
                        id_ = float(id_)
                    except ValueError:
                        raise ValueError(('You set ids_to_floats to true,'
                                          ' but ID {} could not be '
                                          'converted to float in '
                                          '{}').format(id_,
                                                       self.path_or_list))
                ids.append(id_)
                labels.append(class_)
                if ex_num % 100 == 0:
                    self._print_progress(ex_num)
            self._print_progress(ex_num)

        # Remember total number of examples for percentage progress meter
        total = ex_num
        if total == 0:
            raise ValueError("No features found in possibly "
                             "empty file '{}'.".format(self.path_or_list))

        # Convert everything to numpy arrays
        ids = np.array(ids)
        labels = np.array(labels)

        def feat_dict_generator():
            with open(self.path_or_list, 'r' if PY3 else 'rb') as f:
                for ex_num, (_, _, feat_dict) in enumerate(self._sub_read(f)):
                    yield feat_dict
                    if ex_num % 100 == 0:
                        self._print_progress('{:.8}%'.format(100 * ((ex_num /
                                                                     total))))
                self._print_progress("100%")

        # Convert everything to numpy arrays
        features = self.vectorizer.fit_transform(feat_dict_generator())

        # Report that loading is complete
        self._print_progress("done", end="\n")

        # Make sure we have the same number of ids, labels, and features
        assert ids.shape[0] == labels.shape[0] == features.shape[0]

        if ids.shape[0] != len(set(ids)):
            raise ValueError('The example IDs are not unique in %s.' %
                             self.path_or_list)

        return FeatureSet(self.path_or_list, ids, labels=labels,
                          features=features, vectorizer=self.vectorizer)


class DictListReader(Reader):
    """
    This class is to facilitate programmatic use of
    ``Learner.predict()`` and other methods that take
    ``FeatureSet`` objects as input. It iterates
    over examples in the same way as other ``Reader`` classes, but uses a
    list of example dictionaries instead of a path to a file.
    """

    def read(self):
        """
        Read examples from list of dictionaries.

        Returns
        -------
        feature_set : skll.FeatureSet
            FeatureSet representing the list of dictionaries we read in.
        """
        ids = []
        labels = []
        feat_dicts = []
        for example_num, example in enumerate(self.path_or_list):
            curr_id = str(example.get("id",
                                      "EXAMPLE_{}".format(example_num)))
            if self.ids_to_floats:
                try:
                    curr_id = float(curr_id)
                except ValueError:
                    raise ValueError(('You set ids_to_floats to true,' +
                                      ' but ID {} could not be ' +
                                      'converted to float in ' +
                                      '{}').format(curr_id, example))
            class_name = (safe_float(example['y'],
                                     replace_dict=self.class_map)
                          if 'y' in example else None)
            example = example['x']
            # Update lists of IDs, labels, and feature dictionaries
            if self.ids_to_floats:
                try:
                    curr_id = float(curr_id)
                except ValueError:
                    raise ValueError(('You set ids_to_floats to true, but ID '
                                      '{} could not be converted to float in '
                                      '{}').format(curr_id, self.path_or_list))
            ids.append(curr_id)
            labels.append(class_name)
            feat_dicts.append(example)
            # Print out status
            if example_num % 100 == 0:
                self._print_progress(example_num)
        # Convert lists to numpy arrays
        ids = np.array(ids)
        labels = np.array(labels)
        features = self.vectorizer.fit_transform(feat_dicts)

        return FeatureSet('converted', ids, labels=labels,
                          features=features, vectorizer=self.vectorizer)


class NDJReader(Reader):

    """
    Reader to create a ``FeatureSet`` instance from a JSONlines/NDJ file.

    If example/instance IDs are included in the files, they
    must be specified as the  "id" key in each JSON dictionary.
    """

    def _sub_read(self, f):
        """
        The function called on the file buffer in the ``read()`` method
        to iterate through rows.

        Parameters
        ----------
        f : file buffer
            A file buffer for an NDJ file.

        Yields
        ------
        curr_id : str
            The current ID for the example.
        class_name : float or str
            The name of the class label for the example.
        example : dict
            The example valued in dictionary format, with 'x'
            as list of features.

        Raises
        ------
        ValueError
            If IDs cannot be converted to floats, and ``ids_to_floats``
            is ``True``.
        """
        for example_num, line in enumerate(f):
            # Remove extraneous whitespace
            line = line.strip()

            # If this is a comment line or a blank line, move on
            if line.startswith('//') or not line:
                continue

            # Process good lines
            example = json.loads(line)
            # Convert all IDs to strings initially,
            # for consistency with csv and megam formats.
            curr_id = str(example.get("id",
                                      "EXAMPLE_{}".format(example_num)))
            class_name = (safe_float(example['y'],
                                     replace_dict=self.class_map)
                          if 'y' in example else None)
            example = example["x"]

            if self.ids_to_floats:
                try:
                    curr_id = float(curr_id)
                except ValueError:
                    raise ValueError(('You set ids_to_floats to true, but' +
                                      ' ID {} could not be converted to ' +
                                      'float').format(curr_id))

            yield curr_id, class_name, example


class MegaMReader(Reader):

    """
    Reader to create a ``FeatureSet`` instance from  a MegaM -fvals file.

    If example/instance IDs are included in the files, they must be specified
    as a comment line directly preceding the line with feature values.
    """

    def _sub_read(self, f):
        """
        Parameters
        ----------
        f : file buffer
            A file buffer for an MegaM file.

        Yields
        ------
        curr_id : str
            The current ID for the example.
        class_name : float or str
            The name of the class label for the example.
        example : dict
            The example valued in dictionary format, with 'x'
            as list of features.

        Raises
        ------
        ValueError
            If there are duplicate feature names.
        """
        example_num = 0
        curr_id = 'EXAMPLE_0'
        for line in f:
            # Process encoding
            if not isinstance(line, text_type):
                line = UnicodeDammit(line, ['utf-8',
                                            'windows-1252']).unicode_markup
            line = line.strip()
            # Handle instance lines
            if line.startswith('#'):
                curr_id = line[1:].strip()
            elif line and line not in ['TRAIN', 'TEST', 'DEV']:
                split_line = line.split()
                num_cols = len(split_line)
                del line
                # Line is just a class label
                if num_cols == 1:
                    class_name = safe_float(split_line[0],
                                            replace_dict=self.class_map)
                    field_pairs = []
                # Line has a class label and feature-value pairs
                elif num_cols % 2 == 1:
                    class_name = safe_float(split_line[0],
                                            replace_dict=self.class_map)
                    field_pairs = split_line[1:]
                # Line just has feature-value pairs
                elif num_cols % 2 == 0:
                    class_name = None
                    field_pairs = split_line

                curr_info_dict = {}
                if len(field_pairs) > 0:
                    # Get current instances feature-value pairs
                    field_names = islice(field_pairs, 0, None, 2)
                    # Convert values to floats, because otherwise
                    # features'll be categorical
                    field_values = (safe_float(val) for val in
                                    islice(field_pairs, 1, None, 2))

                    # Add the feature-value pairs to dictionary
                    curr_info_dict.update(zip(field_names, field_values))

                    if len(curr_info_dict) != len(field_pairs) / 2:
                        raise ValueError(('There are duplicate feature ' +
                                          'names in {} for example ' +
                                          '{}.').format(self.path_or_list,
                                                        curr_id))

                yield curr_id, class_name, curr_info_dict

                # Set default example ID for next instance, in case we see a
                # line without an ID.
                example_num += 1
                curr_id = 'EXAMPLE_{}'.format(example_num)


class LibSVMReader(Reader):

    """
    Reader to create a ``FeatureSet`` instance from a LibSVM/LibLinear/SVMLight file.

    We use a specially formatted comment for storing example IDs, class names,
    and feature names, which are normally not supported by the format.  The
    comment is not mandatory, but without it, your labels and features will
    not have names.  The comment is structured as follows::

        ExampleID | 1=FirstClass | 1=FirstFeature 2=SecondFeature
    """

    line_regex = re.compile(r'^(?P<label_num>[^ ]+)\s+(?P<features>[^#]*)\s*'
                            r'(?P<comments>#\s*(?P<example_id>[^|]+)\s*\|\s*'
                            r'(?P<label_map>[^|]+)\s*\|\s*'
                            r'(?P<feat_map>.*)\s*)?$', flags=re.UNICODE)

    LIBSVM_REPLACE_DICT = {'\u2236': ':',
                           '\uFF03': '#',
                           '\u2002': ' ',
                           '\ua78a': '=',
                           '\u2223': '|'}

    @staticmethod
    def _pair_to_tuple(pair, feat_map):
        """
        Split a feature-value pair separated by a colon into a tuple.  Also
        do safe_float conversion on the value.

        Parameters
        ----------
        feat_map : str
            A feature-value pair to split.

        Returns
        -------
        name : str
            The name of the feature.
        value
            The value of the example.
        """
        name, value = pair.split(':')
        if feat_map is not None:
            name = feat_map[name]
        value = safe_float(value)
        return (name, value)

    def _sub_read(self, f):
        """
        Parameters
        ----------
        f : file buffer
            A file buffer for an LibSVM file.

        Yields
        ------
        curr_id : str
            The current ID for the example.
        class_name : float or str
            The name of the class label for the example.
        example : dict
            The example valued in dictionary format, with 'x'
            as list of features.

        Raises
        ------
        ValueError
            If line does not look like valid libsvm format.
        """
        for example_num, line in enumerate(f):
            curr_id = ''
            # Decode line if it's not already str
            if isinstance(line, bytes):
                line = UnicodeDammit(line, ['utf-8',
                                            'windows-1252']).unicode_markup
            match = self.line_regex.search(line.strip())
            if not match:
                raise ValueError('Line does not look like valid libsvm format'
                                 '\n{}'.format(line))
            # Metadata is stored in comments if this was produced by SKLL
            if match.group('comments') is not None:
                # Store mapping from feature numbers to names
                if match.group('feat_map'):
                    feat_map = {}
                    for pair in match.group('feat_map').split():
                        number, name = pair.split('=')
                        for orig, replacement in \
                                LibSVMReader.LIBSVM_REPLACE_DICT.items():
                            name = name.replace(orig, replacement)
                        feat_map[number] = name
                else:
                    feat_map = None
                # Store mapping from label/class numbers to names
                if match.group('label_map'):
                    label_map = dict(pair.split('=') for pair in
                                     match.group('label_map').strip().split())
                else:
                    label_map = None
                curr_id = match.group('example_id').strip()

            if not curr_id:
                curr_id = 'EXAMPLE_{}'.format(example_num)

            class_num = match.group('label_num')
            # If we have a mapping from class numbers to labels, get label
            if label_map:
                class_name = label_map[class_num]
            else:
                class_name = class_num
            class_name = safe_float(class_name,
                                    replace_dict=self.class_map)

            curr_info_dict = dict(self._pair_to_tuple(pair, feat_map) for pair
                                  in match.group('features').strip().split())

            yield curr_id, class_name, curr_info_dict


class DelimitedReader(Reader):

    """
    Reader for creating a ``FeatureSet`` instance from a delimited (CSV/TSV) file.

    If example/instance IDs are included in the files, they
    must be specified in the ``id`` column.

    For ARFF, CSV, and TSV files, there must be a column with the
    name specified by ``label_col`` if the data is labeled. For ARFF files,
    this column must also be the final one (as it is in Weka).

    Parameters
    ----------
    path_or_list : str
        The path to a delimited file.
    dialect : str
        The dialect of to pass on to the underlying CSV reader.
        Defaults to ``'excel-tab'``.
    kwargs : dict, optional
        Other arguments to the Reader object.
    """

    def __init__(self, path_or_list, **kwargs):
        self.dialect = kwargs.pop('dialect', 'excel-tab')
        super(DelimitedReader, self).__init__(path_or_list, **kwargs)

    def _sub_read(self, f):
        """
        Parameters
        ----------
        f : file buffer
            A file buffer for an delimited file.

        Yields
        ------
        curr_id : str
            The current ID for the example.
        class_name : float or str
            The name of the class label for the example.
        example : dict
            The example valued in dictionary format, with 'x'
            as list of features.
        """
        reader = DictReader(f, dialect=self.dialect)
        for example_num, row in enumerate(reader):
            if self.label_col is not None and self.label_col in row:
                class_name = safe_float(row[self.label_col],
                                        replace_dict=self.class_map)
                del row[self.label_col]
            else:
                class_name = None

            if self.id_col not in row:
                curr_id = "EXAMPLE_{}".format(example_num)
            else:
                curr_id = row[self.id_col]
                del row[self.id_col]

            # Convert features to floats and if a feature is 0
            # then store the name of the feature so we can
            # delete it later since we don't need to explicitly
            # store zeros in the feature hash
            columns_to_delete = []
            if PY2:
                columns_to_convert_to_unicode = []
            for fname, fval in iteritems(row):
                fval_float = safe_float(fval)
                # we don't need to explicitly store zeros
                if fval_float:
                    row[fname] = fval_float
                    if PY2:
                        columns_to_convert_to_unicode.append(fname)
                else:
                    columns_to_delete.append(fname)

            # remove the columns with zero values
            for cname in columns_to_delete:
                del row[cname]

            # convert the names of all the other columns to
            # unicode for python 2
            if PY2:
                for cname in columns_to_convert_to_unicode:
                    fval = row[cname]
                    del row[cname]
                    row[cname.decode('utf-8')] = fval
                if not self.ids_to_floats:
                    curr_id = curr_id.decode('utf-8')

            yield curr_id, class_name, row


class CSVReader(DelimitedReader):

    """
    Reader for creating a ``FeatureSet`` instance from a CSV file.

    If example/instance IDs are included in the files, they
    must be specified in the ``id`` column.

    Also, there must be a column with the name specified by ``label_col`` if the
    data is labeled.

    Parameters
    ----------
    path_or_list : str
        The path to a comma-delimited file.
    kwargs : dict, optional
        Other arguments to the Reader object.
    """

    def __init__(self, path_or_list, **kwargs):
        kwargs['dialect'] = 'excel'
        super(CSVReader, self).__init__(path_or_list, **kwargs)


class ARFFReader(DelimitedReader):

    """
    Reader for creating a ``FeatureSet`` instance from an ARFF file.

    If example/instance IDs are included in the files, they
    must be specified in the ``id`` column.

    Also, there must be a column with the name specified by ``label_col`` if the
    data is labeled, and this column must be the final one (as it is in Weka).

    Parameters
    ----------
    path_or_list : str
        The path to the ARFF file.
    kwargs : dict, optional
        Other arguments to the Reader object.
    """

    def __init__(self, path_or_list, **kwargs):
        kwargs['dialect'] = 'arff'
        super(ARFFReader, self).__init__(path_or_list, **kwargs)
        self.relation = ''
        self.regression = False

    @staticmethod
    def split_with_quotes(s, delimiter=' ', quote_char="'", escape_char='\\'):
        """
        A replacement for string.split that won't split delimiters enclosed in
        quotes.

        Parameters
        ----------
        s : str
            The string with quotes to split
        delimiter : str, optional
            The delimiter to split on.
            Defaults to ``' '``.
        quote_char : str, optional
            The quote character to ignore.
            Defaults to ``"'"``.
        escape_char : str, optional
            The escape character.
            Defaults to ``'\\'``.
        """
        if PY2:
            delimiter = delimiter.encode()
            quote_char = quote_char.encode()
            escape_char = escape_char.encode()
        return next(csv.reader([s], delimiter=delimiter, quotechar=quote_char,
                               escapechar=escape_char))

    def _sub_read(self, f):
        """
        Parameters
        ----------
        f : file buffer
            A file buffer for the ARFF file.

        Yields
        ------
        curr_id : str
            The current ID for the example.
        class_name : float or str
            The name of the class label for the example.
        example : dict
            The example valued in dictionary format, with 'x'
            as list of features.
        """
        field_names = []
        # Process ARFF header
        for line in f:
            # Process encoding
            if not isinstance(line, text_type):
                decoded_line = UnicodeDammit(line,
                                             ['utf-8',
                                              'windows-1252']).unicode_markup
            else:
                decoded_line = line
            line = decoded_line.strip()
            # Skip empty lines
            if line:
                # Split the line using CSV reader because it can handle
                # quoted delimiters.
                split_header = self.split_with_quotes(line)
                row_type = split_header[0].lower()
                if row_type == '@attribute':
                    # Add field name to list
                    field_name = split_header[1]
                    field_names.append(field_name)
                    # Check if we're doing regression
                    if field_name == self.label_col:
                        self.regression = (len(split_header) > 2 and
                                           split_header[2] == 'numeric')
                # Save relation if specified
                elif row_type == '@relation':
                    self.relation = split_header[1]
                # Stop at data
                elif row_type == '@data':
                    break
                # Skip other types of rows (relations)

        # Create header for CSV
        if PY2:
            io_type = BytesIO
        else:
            io_type = StringIO
        with io_type() as field_buffer:
            csv.writer(field_buffer, dialect='arff').writerow(field_names)
            field_str = field_buffer.getvalue()

        # Set label_col to be the name of the last field, since that's standard
        # for ARFF files
        if self.label_col != field_names[-1]:
            self.label_col = None

        # Process data as CSV file
        return super(ARFFReader, self)._sub_read(chain([field_str], f))


class TSVReader(DelimitedReader):

    """
    Reader for creating a ``FeatureSet`` instance from a TSV file.

    If example/instance IDs are included in the files, they
    must be specified in the ``id`` column.

    Also there must be a column with the name specified by ``label_col``
    if the data is labeled.

    Parameters
    ----------
    path_or_list : str
        The path to the TSV file.
    kwargs : dict, optional
        Other arguments to the Reader object.
    """

    def __init__(self, path_or_list, **kwargs):
        kwargs['dialect'] = 'excel-tab'
        super(TSVReader, self).__init__(path_or_list, **kwargs)


def safe_float(text, replace_dict=None, logger=None):
    """
    Attempts to convert a string to an int, and then a float, but if neither is
    possible, returns the original string value.

    Parameters
    ----------
    text : str
        The text to convert.
    replace_dict : dict, optional
        Mapping from text to replacement text values. This is
        mainly used for collapsing multiple labels into a
        single class. Replacing happens before conversion to
        floats. Anything not in the mapping will be kept the
        same.
        Defaults to ``None``.
    logger : logging.Logger
        The Logger instance to use to log messages. Used instead of
        creating a new Logger instance by default.
        Defaults to ``None``.

    Returns
    -------
    text : int or float or str
        The text value converted to int or float, if possible
    """

    # convert to text to be "Safe"!
    text = text_type(text)

    # get a logger unless we are passed one
    if not logger:
        logger = logging.getLogger(__name__)

    if replace_dict is not None:
        if text in replace_dict:
            text = replace_dict[text]
        else:
            logger.warning('Encountered value that was not in replacement '
                           'dictionary (e.g., class_map): {}'.format(text))
    try:
        return int(text)
    except ValueError:
        try:
            return float(text)
        except ValueError:
            return text.decode('utf-8') if PY2 else text
        except TypeError:
            return 0.0
    except TypeError:
        return 0


# Constants
EXT_TO_READER = {".arff": ARFFReader,
                 ".csv": CSVReader,
                 ".jsonlines": NDJReader,
                 ".libsvm": LibSVMReader,
                 ".megam": MegaMReader,
                 '.ndj': NDJReader,
                 ".tsv": TSVReader}