# License: BSD 3 clause
"""
Load features and labels from various file types.
Handle loading data from various types of data files. A
base ``Reader`` class is provided that is sub-classed for each data
file type that is supported, e.g. ``CSVReader``.
Notes about IDs & Label Conversion
-----------------------------------
All ``Reader`` sub-classes are designed to read in example IDs
as strings unless ``ids_to_floats`` is set to ``True`` in which
case they will be read in as floats, if possible. In the latter
case, an exception will be raised if they cannot be converted to
floats.
All ``Reader`` sub-classes also use the ``safe_float`` function internally
to read in labels. This function tries to convert a single label
first to ``int``, then to ``float``. If neither conversion is
possible, the label remains a ``str``. It should be noted that, if
classification is being done with a feature set that is read in with
one of the ``Reader`` sub-classes, care must be taken to ensure that
labels do not get converted in unexpected ways. For example,
classification labels should not be a mixture of ``int``-converting
and ``float``-converting labels. Consider the situation below:
>>> import numpy as np
>>> from skll.data.readers import safe_float
>>> np.array([safe_float(x) for x in ["2", "2.2", "2.21"]]) # array([2. , 2.2 , 2.21])
The labels will all be converted to floats and any classification
model generated with this data will predict labels such as ``2.0``,
``2.2``, etc., not ``str`` values that exactly match the input
labels, as might be expected. Be aware that it may be best to make
use of the ``class_map`` keyword argument in such cases to map
original labels to labels that convert only to ``str``.
:author: Dan Blanchard (dblanchard@ets.org)
:author: Michael Heilman (mheilman@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)
:author: Jeremy Biggs (jbiggs@ets.org)
:organization: ETS
"""
import csv
import json
import logging
import re
import sys
from io import StringIO
from itertools import chain
from numbers import Number
from pathlib import Path
from typing import IO, Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from bs4 import UnicodeDammit
from sklearn.feature_extraction import FeatureHasher
from skll.data import FeatureSet
from skll.data.dict_vectorizer import DictVectorizer
from skll.types import ClassMap, FeatGenerator, FeatureDictList, IdType, LabelType, PathOrStr
[docs]
class Reader(object):
"""
Load FeatureSets from files on disk.
This is the base class used to create featureset readers for different
file types.
Parameters
----------
path_or_list : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]
Path or a list of example dictionaries.
quiet : bool, default=True
Do not print "Loading..." status message to stderr.
ids_to_floats : bool, default=False
Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
label_col : Optional[str], default='y'
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.
id_col : str, default='id'
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.
class_map : Optional[:class:`skll.types.ClassMap`], default=None
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.
The keys are the new labels and the list of values for each
key is the labels to be collapsed to said new label.
sparse : bool, default=True
Whether or not to store the features in a numpy CSR
matrix when using a DictVectorizer to vectorize the
features.
feature_hasher : bool, default=False
Whether or not a FeatureHasher should be used to
vectorize the features.
num_features : Optional[int], default=None
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.
logger : Optional[logging.Logger], default=None
A logger instance to use to log messages instead of creating
a new one by default.
"""
def __init__(
self,
path_or_list: Union[PathOrStr, FeatureDictList],
quiet: bool = True,
ids_to_floats: bool = False,
label_col: Optional[str] = "y",
id_col: str = "id",
class_map: Optional[ClassMap] = None,
sparse: bool = True,
feature_hasher: bool = False,
num_features: Optional[int] = None,
logger: Optional[logging.Logger] = None,
):
"""Initialize the base class."""
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 = ""
self._use_pandas = False
self.vectorizer: Union[DictVectorizer, FeatureHasher]
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__)
[docs]
@classmethod
def for_path(cls, path_or_list: Union[PathOrStr, FeatureDictList], **kwargs) -> "Reader":
"""
Instantiate Reader sub-class based on the file extension.
If the input is a list of dictionaries instead of a path, use a
dictionary reader instead.
Parameters
----------
path_or_list : Union[:class:`skll.types.PathOrStr`, :class:`skll.types.FeatureDictList`]
A path or list of example dictionaries.
kwargs : Optional[Dict[str, Any]]
The arguments to the Reader object being instantiated.
Returns
-------
reader : :class:`skll.data.readers.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, (str, Path)):
return DictListReader(path_or_list)
else:
# Get lowercase extension for file extension checking
path_or_list = Path(path_or_list)
ext = path_or_list.suffix.lower()
if ext not in EXT_TO_READER:
raise ValueError(
"Example files must be in either .arff, "
".csv, .jsonlines, .ndj, or .tsv format. You"
f"specified: {path_or_list}"
)
return EXT_TO_READER[ext](path_or_list, **kwargs)
def _sub_read(self, file):
"""
Perform the actual reading of the given file or list.
For ``Reader`` objects that do not rely on ``pandas``
(and therefore read row-by-row), this function will
be called by ``_sub_read_rows()`` and will take a file
buffer rather than a file path. Otherwise, it will
take a path and will be called directly in the ``read()``
method.
Parameters
----------
file : Ignored
Not used.
Raises
------
NotImplementedError
"""
raise NotImplementedError
def _print_progress(self, progress_num: Union[int, str], end="\r"):
r"""
Print out progress numbers in proper format.
Nothing gets printed if ``self.quiet`` is ``True``.
Parameters
----------
progress_num: Union[int, str]
Progress indicator value. Usually either a line
number or a percentage. Must be able to convert to string.
end : str, default='\r'
The string to put at the end of the line. "\r" should be
used for every update except for the final one.
"""
# Print out status
if not self.quiet:
print(f"{self._progress_msg}{progress_num:>15}", end=end, file=sys.stderr)
sys.stderr.flush()
def _sub_read_rows(self, file: PathOrStr) -> Tuple[np.ndarray, np.ndarray, FeatureDictList]:
"""
Read the file in row-by-row.
This method is used for ``Reader`` objects that do not rely on ``pandas``,
and are instead read line-by-line into a FeatureSet object, unlike
pandas-based reader object, which will read everything into memory in
a data frame object before converting to a ``FeatureSet``.
Parameters
----------
file : :class:`skll.types.PathOrStr`
The path to the input file.
Returns
-------
ids : numpy.ndarray of shape (n_ids,)
The ids array.
labels : numpy.ndarray of shape (n_labels,)
The labels array.
features : :class:`skll.types.FeatureDictList`
List containing feature dictionaries.
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.
"""
# Get labels and IDs
ids_list: List[IdType] = []
labels_list: List[LabelType] = []
ex_num = 0
with open(file, encoding="utf-8") as f:
for ex_num, (id_, class_, _) in enumerate(self._sub_read(f), start=1):
# Update lists of IDs, classes, and features
if self.ids_to_floats:
try:
id_ = float(id_)
except ValueError:
raise ValueError(
"You set ids_to_floats to true, but "
f"ID {id_} could not be converted to"
f" float in {self.path_or_list}"
)
ids_list.append(id_)
labels_list.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 " f"'{self.path_or_list}'.")
# Convert everything to numpy arrays
ids = np.array(ids_list)
labels = np.array(labels_list)
def feat_dict_generator():
with open(self.path_or_list, encoding="utf-8") as f:
for ex_num, (_, _, feat_dict) in enumerate(self._sub_read(f)):
yield feat_dict
if ex_num % 100 == 0:
self._print_progress(f"{100 * ex_num / total:.8}%")
self._print_progress("100%")
# extract the features dictionary
features = feat_dict_generator()
return ids, labels, features
def _parse_dataframe(
self,
df: pd.DataFrame,
id_col: Optional[str],
label_col: Optional[str],
replace_blanks_with: Optional[Union[Number, Dict[str, Number]]] = None,
drop_blanks: bool = False,
) -> Tuple[np.ndarray, np.ndarray, FeatureDictList]:
"""
Parse the data frame into ids, labels, and features.
For ``Reader`` objects that rely on ``pandas``, this function
will be called in the ``_sub_read()`` method to parse the
data frame into the expected format. It will not be used
by ``Reader`` classes that read row-by-row (and therefore
use the ``_sub_read_rows()`` function).
Parameters
----------
df : pandas.DataFrame
The pandas data frame to parse.
id_col : Optional[str]
The id column.
label_col : Optional[str]
The label column.
replace_blanks_with : Optional[Union[Number, Dict[str, Number]]], default=None
Specifies a new value with which to replace blank values.
Options are:
- ``Number`` : A (numeric) value with which to replace blank values.
- ``Dict[str, Number]`` : A dictionary specifying the replacemen
value for each column.
- ``None`` : Blank values will be left as blanks, and not replaced.
drop_blanks : bool, default=False
If ``True``, remove lines/rows that have any blank values.
Returns
-------
ids : numpy.ndarray of shape (n_ids,)
The ids for the feature set.
labels : numpy.ndarray of shape (n_labels,)
The labels for the feature set.
features : :class:`skll.types.FeatureDictList`
List of feature dictionaries.
"""
if df.empty:
raise ValueError("No features found in possibly empty file " f"'{self.path_or_list}'.")
if drop_blanks and replace_blanks_with is not None:
raise ValueError(
"You cannot both drop blanks and replace them. "
"'replace_blanks_with' can only have a value when "
"'drop_blanks' is `False`."
)
# should we replace blank values with something?
if replace_blanks_with is not None:
self.logger.info(
"Blank values in all rows/lines will be replaced with " "user-specified value(s)."
)
df = df.fillna(replace_blanks_with)
# should we remove lines that have any NaNs?
if drop_blanks:
self.logger.info("Rows/lines with any blank values will be dropped.")
df = df.dropna().reset_index(drop=True)
# if the dataframe has no rows left after removing blanks,
# raise an exception here because downstream processing
# will run into issues
if df.empty:
raise ValueError(
"No rows/lines left in the feature file " "after dropping blank values."
)
# if the id column exists,
# get them from the data frame and
# delete the column; otherwise, just
# set it to None
if id_col is not None and id_col in df:
ids = df[id_col].astype(str)
del df[id_col]
# if `ids_to_floats` is True,
# then convert the ids to floats
if self.ids_to_floats:
ids = ids.astype(float)
ids = ids.values
else:
# create ids with the prefix `EXAMPLE_`
ids = np.array([f"EXAMPLE_{i}" for i in range(df.shape[0])])
# if the label column exists,
# get them from the data frame and
# delete the column; otherwise, just
# set it to None
if label_col is not None and label_col in df:
labels = df[label_col]
del df[label_col]
# if `class_map` exists, then
# map the new classes to the labels;
# otherwise, just convert them to floats
if self.class_map is not None:
labels = labels.apply(safe_float, replace_dict=self.class_map)
else:
labels = labels.apply(safe_float)
labels = labels.values
else:
# create an array of Nones
labels = np.array([None] * df.shape[0])
# convert the remaining features to
# a list of dictionaries
features = df.to_dict(orient="records")
return ids, labels, features
[docs]
def read(self) -> FeatureSet:
"""
Load examples from various file formats.
The following formats are supported: ``.arff``, ``.csv``, ``.jsonlines``,
``.libsvm``, ``.ndj``, or ``.tsv`` formats.
Returns
-------
:class:`skll.data.featureset.FeatureSet`
A ``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(f"Path: {self.path_or_list}")
if not self.quiet:
self._progress_msg = f"Loading {self.path_or_list}..."
print(self._progress_msg, end="\r", file=sys.stderr)
sys.stderr.flush()
# if we are in this method, self.path_or_file must be a path
assert isinstance(self.path_or_list, (str, Path)), "file path or path object required"
if self._use_pandas:
ids, labels, features = self._sub_read(self.path_or_list)
else:
ids, labels, features = self._sub_read_rows(self.path_or_list)
# Convert everything to numpy arrays
features = self.vectorizer.fit_transform(features)
# 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 " f"{self.path_or_list}.")
featureset_name = str(self.path_or_list)
return FeatureSet(
featureset_name, ids, labels=labels, features=features, vectorizer=self.vectorizer
)
[docs]
class DictListReader(Reader):
"""
Facilitate programmatic use of methods that take ``FeatureSet`` as input.
Support ``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.
Parameters
----------
path_or_list : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]
Path or a list of example dictionaries.
quiet : bool, default=True
Do not print "Loading..." status message to stderr.
ids_to_floats : bool, default=False
Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
label_col : Optional[str], default='y'
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.
id_col : str, default='id'
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.
class_map : Optional[:class:`skll.types.ClassMap`], default=None
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.
The keys are the new labels and the list of values for each
key is the labels to be collapsed to said new label.
sparse : bool, default=True
Whether or not to store the features in a numpy CSR
matrix when using a DictVectorizer to vectorize the
features.
feature_hasher : bool, default=False
Whether or not a FeatureHasher should be used to
vectorize the features.
num_features : Optional[int], default=None
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.
logger : Optional[logging.Logger], default=None
A logger instance to use to log messages instead of creating
a new one by default.
"""
[docs]
def read(self) -> FeatureSet:
"""
Read examples from list of dictionaries.
Returns
-------
:class:`skll.data.FeatureSet`
A ``FeatureSet`` representing the list of dictionaries we read in.
"""
# if we are in this method, `self.path_or_list` must be a
# list of dictionaries
assert isinstance(self.path_or_list, list)
# initialize some variables
ids_list: List[IdType] = []
labels_list: List[Optional[LabelType]] = []
feat_dicts: FeatureDictList = []
for example_num, example in enumerate(self.path_or_list):
curr_id: Union[float, str] = str(example.get("id", f"EXAMPLE_{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 "
f"{curr_id} could not be converted to "
f"float in {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 "
f"{curr_id} could not be converted to "
f"float in {self.path_or_list}"
)
ids_list.append(curr_id)
labels_list.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_list)
labels = np.array(labels_list)
features = self.vectorizer.fit_transform(feat_dicts)
return FeatureSet(
"converted", ids, labels=labels, features=features, vectorizer=self.vectorizer
)
[docs]
class NDJReader(Reader):
"""
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.
Parameters
----------
path_or_list : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]
Path or a list of example dictionaries.
quiet : bool, default=True
Do not print "Loading..." status message to stderr.
ids_to_floats : bool, default=False
Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
label_col : Optional[str], default='y'
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.
id_col : str, default='id'
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.
class_map : Optional[:class:`skll.types.ClassMap`], default=None
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.
The keys are the new labels and the list of values for each
key is the labels to be collapsed to said new label.
sparse : bool, default=True
Whether or not to store the features in a numpy CSR
matrix when using a DictVectorizer to vectorize the
features.
feature_hasher : bool, default=False
Whether or not a FeatureHasher should be used to
vectorize the features.
num_features : Optional[int], default=None
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.
logger : Optional[logging.Logger], default=None
A logger instance to use to log messages instead of creating
a new one by default.
"""
def _sub_read(self, file: IO[str]) -> FeatGenerator:
"""
Iterate through the rows of the file buffer.
Parameters
----------
file : IO[str]
A file buffer for an NDJ file.
Yields
------
curr_id : :class:`skll.types.IdType`
The current ID for the example.
class_name : Optional[:class:`skll.types.LabelType`]
The name of the class label for the example.
example : :class:`skll.types.FeatureDict`
The example value 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(file):
# 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 formats.
curr_id: IdType = str(example.get("id", f"EXAMPLE_{example_num}"))
class_name: Optional[Union[float, str]] = (
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 "
f"{curr_id} could not be converted to "
"float"
)
yield curr_id, class_name, example
[docs]
class LibSVMReader(Reader):
"""
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
Parameters
----------
path_or_list : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]
Path or a list of example dictionaries.
quiet : bool, default=True
Do not print "Loading..." status message to stderr.
ids_to_floats : bool, default=False
Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
label_col : Optional[str], default='y'
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.
id_col : str, default='id'
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.
class_map : Optional[:class:`skll.types.ClassMap`], default=None
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.
The keys are the new labels and the list of values for each
key is the labels to be collapsed to said new label.
sparse : bool, default=True
Whether or not to store the features in a numpy CSR
matrix when using a DictVectorizer to vectorize the
features.
feature_hasher : bool, default=False
Whether or not a FeatureHasher should be used to
vectorize the features.
num_features : Optional[int], default=None
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.
logger : Optional[logging.Logger], default=None
A logger instance to use to log messages instead of creating
a new one by default.
"""
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: str, feat_map: Dict[str, str]) -> Tuple[str, Union[float, int, str]]:
"""
Split feature-value pair separated by a colon into tuple.
Also perform `safe_float` conversion on the value.
Parameters
----------
pair: str
A feature-value pair to split.
feat_map : Dict[str, str]
Feature name mapping.
Returns
-------
name : str
The name of the feature.
value : Union[float, int, str]
The value of the example.
"""
name, value = pair.split(":")
if feat_map is not None:
ret_name = feat_map[name]
ret_value = safe_float(value)
return (ret_name, ret_value)
def _sub_read(self, file: IO[str]) -> FeatGenerator:
"""
Parse rows of LibSVM file.
Parameters
----------
file : IO[str]
A file buffer for an LibSVM file.
Yields
------
curr_id : :class:`skll.types.IdType`
The current ID for the example.
class_ : :class:`skll.types.LabelType`
The name of the class label for the example.
example : :class:`skll.types.FeatureDict`
The example valued in dictionary format, with 'x'
as list of features.
Raises
------
ValueError
If line does not look like valid libsvm format.
"""
feat_map: Optional[Dict[str, str]]
for example_num, line in enumerate(file):
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" f"\n{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 = f"EXAMPLE_{example_num}"
# the class can be either a float, an int, or a string;
# so we can use our `LabelType` type alias for this
class_: LabelType
# get the class number
class_num = match.group("label_num")
# If we have a mapping from class numbers to labels, get label
if label_map:
class_ = label_map[class_num]
else:
class_ = class_num
class_ = safe_float(class_, replace_dict=self.class_map)
if feat_map:
curr_info_dict = dict(
self._pair_to_tuple(pair, feat_map)
for pair in match.group("features").strip().split()
)
else:
curr_info_dict = {}
yield curr_id, class_, curr_info_dict
[docs]
class CSVReader(Reader):
"""
Create 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 : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]]
The path to a comma-delimited file.
replace_blanks_with : Optional[Union[Number, Dict[str, Number]]], default=None
Specifies a new value with which to replace blank values.
Options are:
- ``Number`` : A (numeric) value with which to replace blank values.
- ``dict`` : A dictionary specifying the replacement value for each column.
- ``None`` : Blank values will be left as blanks, and not replaced.
The replacement occurs after the data set is read into a ``pd.DataFrame``.
drop_blanks : bool, default=False
If ``True``, remove lines/rows that have any blank
values. These lines/rows are removed after the
the data set is read into a ``pd.DataFrame``.
pandas_kwargs : Optional[Dict[str, Any]], default=None
Arguments that will be passed directly to the ``pandas`` I/O reader.
kwargs : Optional[Dict[str, Any]]
Other arguments to the Reader object.
"""
def __init__(
self,
path_or_list: Union[PathOrStr, List[Dict[str, Any]]],
replace_blanks_with: Optional[Union[Number, Dict[str, Number]]] = None,
drop_blanks: bool = False,
pandas_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
):
"""Initialize CSVReader class."""
super(CSVReader, self).__init__(path_or_list, **kwargs)
self._replace_blanks_with = replace_blanks_with
self._drop_blanks = drop_blanks
self._pandas_kwargs = {} if pandas_kwargs is None else pandas_kwargs
self._sep = self._pandas_kwargs.pop("sep", str(","))
self._engine = self._pandas_kwargs.pop("engine", "c")
self._use_pandas = True
def _sub_read(self, file: PathOrStr) -> Tuple[np.ndarray, np.ndarray, FeatureDictList]:
"""
Parse rows of CSV file.
Parameters
----------
file : :class:`skll.types.PathOrStr`
The path to the CSV file.
Returns
-------
ids : numpy.ndarray of shape (n_ids,)
The ids for the feature set.
labels : numpy.ndarray of shape (n_labels,)
The labels for the feature set.
features : :class:`skll.types.FeatureDictList`
The list of feature dictionaries for the feature set.
"""
df = pd.read_csv(file, sep=self._sep, engine=self._engine, **self._pandas_kwargs)
return self._parse_dataframe(
df,
self.id_col,
self.label_col,
replace_blanks_with=self._replace_blanks_with,
drop_blanks=self._drop_blanks,
)
[docs]
class TSVReader(CSVReader):
"""
Create 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 a comma-delimited file.
replace_blanks_with : Optional[Union[Number, Dict[str, Number]]], default=None
Specifies a new value with which to replace blank values.
Options are:
- ``Number`` : A (numeric) value with which to replace blank values.
- ``dict`` : A dictionary specifying the replacement value for each column.
- ``None`` : Blank values will be left as blanks, and not replaced.
The replacement occurs after the data set is read into a ``pd.DataFrame``.
drop_blanks : bool, default=False
If ``True``, remove lines/rows that have any blank values. These
lines/rows are removed after the the data set is read into a
``pd.DataFrame``.
pandas_kwargs : Optional[Dict[str, Any]], default=None
Arguments that will be passed directly to the ``pandas`` I/O reader.
kwargs : Optional[Dict[str, Any]]
Other arguments to the Reader object.
"""
def __init__(
self,
path_or_list: Union[PathOrStr, List[Dict[str, Any]]],
replace_blanks_with: Optional[Union[Number, Dict[str, Number]]] = None,
drop_blanks: bool = False,
pandas_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
):
"""Initialize TSVReader class."""
super(TSVReader, self).__init__(
path_or_list,
replace_blanks_with=replace_blanks_with,
drop_blanks=drop_blanks,
pandas_kwargs=pandas_kwargs,
**kwargs,
)
self._sep = str("\t")
[docs]
class ARFFReader(Reader):
"""
Create 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 : Union[:class:`skll.types.PathOrStr`, List[Dict[str, Any]]]
The path to the ARFF file.
kwargs : Optional[Dict[str, Any]]
Other arguments to the Reader object.
"""
def __init__(self, path_or_list: Union[PathOrStr, List[Dict[str, Any]]], **kwargs):
"""Initialize ARFFReader class."""
super(ARFFReader, self).__init__(path_or_list, **kwargs)
self.dialect = "arff"
self.relation = ""
self.regression = False
[docs]
@staticmethod
def split_with_quotes(
string: str, delimiter: str = " ", quote_char: str = "'", escape_char: str = "\\"
) -> List[str]:
r"""
Split strings but not on split delimiters enclosed in quotes.
Parameters
----------
string : str
The string with quotes to split
delimiter : str, default=' '
The delimiter to split on.
quote_char : str, default="'"
The quote character to ignore.
escape_char : str, default='\\'
The escape character.
"""
return next(
csv.reader([string], delimiter=delimiter, quotechar=quote_char, escapechar=escape_char)
)
def _sub_read(self, file: IO[str]) -> FeatGenerator:
"""
Parse rows of ARFF file.
Parameters
----------
file : IO[str]
A file buffer for the ARFF file.
Yields
------
curr_id : :class:`skll.types.IdType`
The current ID for the example.
class_name : :class:`skll.types.LabelType`
The name of the class label for the example.
example : :class:`skll.types.FeatureDict`
The example features in dictionary format.
"""
field_names = []
# Process ARFF header
for line in file:
# Process encoding
if not isinstance(line, str):
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
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 iterator as a CSV file
csv_file_buffer = chain([field_str], file)
reader = csv.DictReader(csv_file_buffer, 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 = f"EXAMPLE_{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 = []
for fname, fval in row.items():
fval_float = safe_float(fval)
# we don't need to explicitly store zeros
if fval_float:
row[fname] = fval_float
else:
columns_to_delete.append(fname)
# remove the columns with zero values
for cname in columns_to_delete:
del row[cname]
yield curr_id, class_name, row
def safe_float(
text: Any,
replace_dict: Optional[Dict[str, List[str]]] = None,
logger: Optional[logging.Logger] = None,
) -> Union[float, int, str]:
"""
Convert string to a float.
It first tries to convert to an integer and then to a float if that fails.
If neither is possible, the originals string value is returned.
Parameters
----------
text : Any
The text to convert.
replace_dict : Optional[Dict[str, List[str]]], default=None
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.
logger : Optional[logging.Logger], default=None
The Logger instance to use to log messages. Used instead of
creating a new Logger instance by default.
Returns
-------
Union[float, int, str]
The text value converted to int or float, if possible. Otherwise
it's a string.
"""
# convert to str to be "Safe"!
text = str(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 "
f"dictionary (e.g., class_map): {text}"
)
try:
return int(text)
except ValueError:
try:
return float(text)
except ValueError:
return text
except TypeError:
return 0.0
except TypeError:
return 0
# Constants
EXT_TO_READER = {
".arff": ARFFReader,
".csv": CSVReader,
".jsonlines": NDJReader,
".libsvm": LibSVMReader,
".ndj": NDJReader,
".tsv": TSVReader,
}