Source code for skll.data.readers

# 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 map, zip
from sklearn.feature_extraction import FeatureHasher

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


[docs]class Reader(object): """ A little helper class to make picklable iterators out of example dictionary generators :param path_or_list: Path or a list of example dictionaries. :type path_or_list: str or list of dict :param quiet: Do not print "Loading..." status message to stderr. :type quiet: bool :param ids_to_floats: Convert IDs to float to save memory. Will raise error if we encounter an a non-numeric ID. :type ids_to_floats: bool :param id_col: Name of the column which contains the instance IDs for ARFF/CSV/TSV files. If no column with that name exists, or `None` is specified, the IDs will be generated automatically. :type id_col: str :param label_col: 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. :type label_col: str :param class_map: 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. :type class_map: dict from str to str :param sparse: Whether or not to store the features in a numpy CSR matrix when using a DictVectorizer to vectorize the features. :type sparse: bool :param feature_hasher: Whether or not a FeatureHasher should be used to vectorize the features. :type feature_hasher: bool :param num_features: 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. :type num_features: int """ 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): 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) @classmethod
[docs] def for_path(cls, path_or_list, **kwargs): """ :param path: The path to the file to load the examples from, or a list of example dictionaries. :type path: str or dict :param quiet: Do not print "Loading..." status message to stderr. :type quiet: bool :param sparse: Whether or not to store the features in a numpy CSR matrix. :type sparse: bool :param id_col: Name of the column which contains the instance IDs for ARFF/CSV/TSV files. If no column with that name exists, or `None` is specified, the IDs will be generated automatically. :type id_col: str :param label_col: 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. :type label_col: str :param ids_to_floats: Convert IDs to float to save memory. Will raise error if we encounter an a non-numeric ID. :type ids_to_floats: bool :param class_map: Mapping from original class labels to new ones. This is mainly used for collapsing multiple classes into a single class. Anything not in the mapping will be kept the same. :type class_map: dict from str to str :returns: New instance of the :class:`Reader` sub-class that is appropriate for the given path, or :class:`DictListReader` if given a list of dictionaries. """ 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. :param f: An open file to iterate through :type f: file """ raise NotImplementedError def _print_progress(self, progress_num, end="\r"): """ Little helper to print out progress numbers in proper format. Nothing gets printed if ``self.quiet`` is ``True``. :param progress_num: Progress indicator value. Usually either a line number or a percentage. :type progress_num: anything that can be converted to str :param end: The string to put at the end of the line. "\\r" should be used for every update except for the final one. :type end: str """ # Print out status if not self.quiet: print("{}{:>15}".format(self._progress_msg, progress_num), end=end, file=sys.stderr) sys.stderr.flush()
[docs] def read(self): """ Loads examples in the ``.arff``, ``.csv``, ``.jsonlines``, ``.libsvm``, ``.megam``, ``.ndj``, or ``.tsv`` formats. :returns: :class:`~skll.data.featureset.FeatureSet` representing the file we read in. """ # Setup logger logger = logging.getLogger(__name__) 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 = [] 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 # 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)
[docs]class DictListReader(Reader): """ This class is to facilitate programmatic use of :meth:`~skll.learner.Learner.predict` and other functions that take :class:`~skll.data.featureset.FeatureSet` objects as input. It iterates over examples in the same way as other :class:`Reader` clases, but uses a list of example dictionaries instead of a path to a file. :param path_or_list: List of example dictionaries. :type path_or_list: Iterable of dict :param quiet: Do not print "Loading..." status message to stderr. :type quiet: bool :param ids_to_floats: Convert IDs to float to save memory. Will raise error if we encounter an a non-numeric ID. :type ids_to_floats: bool """ def read(self): 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)
[docs]class NDJReader(Reader): """ Reader to create a :class:`~skll.data.featureset.FeatureSet` out of a .jsonlines/.ndj file If you would like to include example/instance IDs in your files, they must be specified in the following ways as an "id" key in each JSON dictionary. """ def _sub_read(self, f): 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
[docs]class MegaMReader(Reader): """ Reader to create a :class:`~skll.data.featureset.FeatureSet` out ouf a MegaM -fvals file. If you would like to include example/instance IDs in your files, they must be specified as a comment line directly preceding the line with feature values. """ def _sub_read(self, f): 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)
[docs]class LibSVMReader(Reader): """ Reader to create a :class:`~skll.data.featureset.FeatureSet` out ouf 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. """ 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): 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
[docs]class DelimitedReader(Reader): """ Reader for creating a :class:`~skll.data.featureset.FeatureSet` out of a delimited (CSV/TSV) file. If you would like to include example/instance IDs in your files, they must be specified as an ``id`` column. Also, for ARFF, CSV, and TSV files, there must be a column with the name specified by `label_col` if the data is labelled. For ARFF files, this column must also be the final one (as it is in Weka). :param dialect: The dialect of to pass on to the underlying CSV reader. Default: ``excel-tab`` :type dialect: str """ 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): 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
[docs]class CSVReader(DelimitedReader): """ Reader for creating a :class:`~skll.data.featureset.FeatureSet` out of a CSV file. If you would like to include example/instance IDs in your files, they must be specified as an "id" column. Also, there must be a column with the name specified by `label_col` if the data is labelled. """ def __init__(self, path_or_list, **kwargs): kwargs['dialect'] = 'excel' super(CSVReader, self).__init__(path_or_list, **kwargs)
[docs]class ARFFReader(DelimitedReader): """ Reader for creating a :class:`~skll.data.featureset.FeatureSet` out of an ARFF file. If you would like to include example/instance IDs in your files, they must be specified as an "id" column. Also, there must be a column with the name specified by `label_col` if the data is labelled, and this column must be the final one (as it is in Weka). """ def __init__(self, path_or_list, **kwargs): kwargs['dialect'] = 'arff' super(ARFFReader, self).__init__(path_or_list, **kwargs) self.relation = '' self.regression = False @staticmethod
[docs] def split_with_quotes(s, delimiter=' ', quote_char="'", escape_char='\\'): """ A replacement for string.split that won't split delimiters enclosed in quotes. """ 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): 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))
[docs]class TSVReader(DelimitedReader): """ Reader for creating a :class:`~skll.data.featureset.FeatureSet` out of a TSV file. If you would like to include example/instance IDs in your files, they must be specified as an "id" column. Also there must be a column with the name specified by `label_col` if the data is labelled. """ def __init__(self, path_or_list, **kwargs): kwargs['dialect'] = 'excel-tab' super(TSVReader, self).__init__(path_or_list, **kwargs)
[docs]def safe_float(text, replace_dict=None): """ Attempts to convert a string to an int, and then a float, but if neither is possible, just returns the original string value. :param text: The text to convert. :type text: str :param replace_dict: 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. :type replace_dict: dict from str to str """ # convert to text to be "Safe"! text = text_type(text) if replace_dict is not None: if text in replace_dict: text = replace_dict[text] else: logging.getLogger(__name__).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}