# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
""" This class handle features definition in datasets and some utilities to display table type."""
import copy
import re
import sys
from collections.abc import Iterable
from dataclasses import dataclass, field, fields
from functools import reduce
from operator import mul
from typing import Any, ClassVar, Dict, List, Optional
from typing import Sequence as Sequence_
from typing import Tuple, Union
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.types
from pandas.api.extensions import ExtensionArray as PandasExtensionArray
from pandas.api.extensions import ExtensionDtype as PandasExtensionDtype
from pyarrow.lib import TimestampType
from pyarrow.types import is_boolean, is_primitive
from . import config, utils
from .utils.logging import get_logger
logger = get_logger(__name__)
def _arrow_to_datasets_dtype(arrow_type: pa.DataType) -> str:
"""
_arrow_to_datasets_dtype takes a pyarrow.DataType and converts it to a datasets string dtype.
In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))`
"""
if pyarrow.types.is_null(arrow_type):
return "null"
elif pyarrow.types.is_boolean(arrow_type):
return "bool"
elif pyarrow.types.is_int8(arrow_type):
return "int8"
elif pyarrow.types.is_int16(arrow_type):
return "int16"
elif pyarrow.types.is_int32(arrow_type):
return "int32"
elif pyarrow.types.is_int64(arrow_type):
return "int64"
elif pyarrow.types.is_uint8(arrow_type):
return "uint8"
elif pyarrow.types.is_uint16(arrow_type):
return "uint16"
elif pyarrow.types.is_uint32(arrow_type):
return "uint32"
elif pyarrow.types.is_uint64(arrow_type):
return "uint64"
elif pyarrow.types.is_float16(arrow_type):
return "float16" # pyarrow dtype is "halffloat"
elif pyarrow.types.is_float32(arrow_type):
return "float32" # pyarrow dtype is "float"
elif pyarrow.types.is_float64(arrow_type):
return "float64" # pyarrow dtype is "double"
elif pyarrow.types.is_timestamp(arrow_type):
assert isinstance(arrow_type, TimestampType)
if arrow_type.tz is None:
return f"timestamp[{arrow_type.unit}]"
elif arrow_type.tz:
return f"timestamp[{arrow_type.unit}, tz={arrow_type.tz}]"
else:
raise ValueError(f"Unexpected timestamp object {arrow_type}.")
elif pyarrow.types.is_binary(arrow_type):
return "binary"
elif pyarrow.types.is_large_binary(arrow_type):
return "large_binary"
elif pyarrow.types.is_string(arrow_type):
return "string"
elif pyarrow.types.is_large_string(arrow_type):
return "large_string"
else:
raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.")
def string_to_arrow(datasets_dtype: str) -> pa.DataType:
"""
string_to_arrow takes a datasets string dtype and converts it to a pyarrow.DataType.
In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))`
This is necessary because the datasets.Value() primitive type is constructed using a string dtype
Value(dtype=str)
But Features.type (via `get_nested_type()` expects to resolve Features into a pyarrow Schema,
which means that each Value() must be able to resolve into a corresponding pyarrow.DataType, which is the
purpose of this function.
"""
timestamp_regex = re.compile(r"^timestamp\[(.*)\]$")
timestamp_matches = timestamp_regex.search(datasets_dtype)
if timestamp_matches:
"""
Example timestamp dtypes:
timestamp[us]
timestamp[us, tz=America/New_York]
"""
timestamp_internals = timestamp_matches.group(1)
internals_regex = re.compile(r"^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+\-:]*)$")
internals_matches = internals_regex.search(timestamp_internals)
if timestamp_internals in ["s", "ms", "us", "ns"]:
return pa.timestamp(timestamp_internals)
elif internals_matches:
return pa.timestamp(internals_matches.group(1), internals_matches.group(2))
else:
raise ValueError(
f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
)
elif datasets_dtype not in pa.__dict__:
if str(datasets_dtype + "_") not in pa.__dict__:
raise ValueError(
f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. "
f"Please make sure to use a correct data type, see: "
f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions"
)
arrow_data_factory_function_name = str(datasets_dtype + "_")
else:
arrow_data_factory_function_name = datasets_dtype
return pa.__dict__[arrow_data_factory_function_name]()
def _cast_to_python_objects(obj: Any) -> Tuple[Any, bool]:
"""
Cast pytorch/tensorflow/pandas objects to python numpy array/lists.
It works recursively.
To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted.
If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same.
This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example.
Args:
obj: the object (nested struct) to cast
Returns:
casted_obj: the casted object
has_changed (bool): True if the object has been changed, False if it is identical
"""
if config.TF_AVAILABLE and "tensorflow" in sys.modules:
import tensorflow as tf
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if config.JAX_AVAILABLE and "jax" in sys.modules:
import jax.numpy as jnp
if isinstance(obj, np.ndarray):
return obj.tolist(), False
elif config.TORCH_AVAILABLE and "torch" in sys.modules and isinstance(obj, torch.Tensor):
return obj.detach().cpu().numpy(), True
elif config.TF_AVAILABLE and "tensorflow" in sys.modules and isinstance(obj, tf.Tensor):
return obj.numpy(), True
elif config.JAX_AVAILABLE and "jax" in sys.modules and isinstance(obj, jnp.ndarray):
return np.asarray(obj), True
elif isinstance(obj, pd.Series):
return obj.values.tolist(), True
elif isinstance(obj, pd.DataFrame):
return obj.to_dict("list"), True
elif isinstance(obj, dict):
output = {}
has_changed = False
for k, v in obj.items():
casted_v, has_changed_v = _cast_to_python_objects(v)
has_changed |= has_changed_v
output[k] = casted_v
return output if has_changed else obj, has_changed
elif isinstance(obj, (list, tuple)):
if len(obj) > 0:
for first_elmt in obj:
if first_elmt is not None:
break
casted_first_elmt, has_changed_first_elmt = _cast_to_python_objects(first_elmt)
if has_changed_first_elmt:
return [_cast_to_python_objects(elmt)[0] for elmt in obj], True
else:
if isinstance(obj, list):
return obj, False
else:
return list(obj), True
else:
return obj if isinstance(obj, list) else [], isinstance(obj, tuple)
else:
return obj, False
def cast_to_python_objects(obj: Any) -> Any:
"""
Cast numpy/pytorch/tensorflow/pandas objects to python lists.
It works recursively.
To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted.
If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same.
This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example.
Args:
obj: the object (nested struct) to cast
Returns:
casted_obj: the casted object
"""
return _cast_to_python_objects(obj)[0]
[docs]@dataclass
class Value:
"""
The Value dtypes are as follows:
null
bool
int8
int16
int32
int64
uint8
uint16
uint32
uint64
float16
float32 (alias float)
float64 (alias double)
timestamp[(s|ms|us|ns)]
timestamp[(s|ms|us|ns), tz=(tzstring)]
binary
large_binary
string
large_string
"""
dtype: str
id: Optional[str] = None
# Automatically constructed
pa_type: ClassVar[Any] = None
_type: str = field(default="Value", init=False, repr=False)
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
self.pa_type = string_to_arrow(self.dtype)
def __call__(self):
return self.pa_type
def encode_example(self, value):
if pa.types.is_boolean(self.pa_type):
return bool(value)
elif pa.types.is_integer(self.pa_type):
return int(value)
elif pa.types.is_floating(self.pa_type):
return float(value)
else:
return value
class _ArrayXD:
def __post_init__(self):
self.shape = tuple(self.shape)
def __call__(self):
pa_type = globals()[self.__class__.__name__ + "ExtensionType"](self.shape, self.dtype)
return pa_type
def encode_example(self, value):
if isinstance(value, np.ndarray):
value = value.tolist()
return value
[docs]@dataclass
class Array2D(_ArrayXD):
shape: tuple
dtype: str
id: Optional[str] = None
# Automatically constructed
_type: str = field(default="Array2D", init=False, repr=False)
[docs]@dataclass
class Array3D(_ArrayXD):
shape: tuple
dtype: str
id: Optional[str] = None
# Automatically constructed
_type: str = field(default="Array3D", init=False, repr=False)
[docs]@dataclass
class Array4D(_ArrayXD):
shape: tuple
dtype: str
id: Optional[str] = None
# Automatically constructed
_type: str = field(default="Array4D", init=False, repr=False)
[docs]@dataclass
class Array5D(_ArrayXD):
shape: tuple
dtype: str
id: Optional[str] = None
# Automatically constructed
_type: str = field(default="Array5D", init=False, repr=False)
class _ArrayXDExtensionType(pa.PyExtensionType):
ndims: Optional[int] = None
def __init__(self, shape: tuple, dtype: str):
assert (
self.ndims is not None and self.ndims > 1
), "You must instantiate an array type with a value for dim that is > 1"
assert len(shape) == self.ndims, "shape={} and ndims={} dom't match".format(shape, self.ndims)
self.shape = tuple(shape)
self.value_type = dtype
self.storage_dtype = self._generate_dtype(self.value_type)
pa.PyExtensionType.__init__(self, self.storage_dtype)
def __reduce__(self):
return self.__class__, (
self.shape,
self.value_type,
)
def __arrow_ext_class__(self):
return ArrayExtensionArray
def _generate_dtype(self, dtype):
dtype = string_to_arrow(dtype)
for d in reversed(self.shape):
dtype = pa.list_(dtype)
# Don't specify the size of the list, since fixed length list arrays have issues
# being validated after slicing in pyarrow 0.17.1
return dtype
def to_pandas_dtype(self):
return PandasArrayExtensionDtype(self.value_type)
class Array2DExtensionType(_ArrayXDExtensionType):
ndims = 2
class Array3DExtensionType(_ArrayXDExtensionType):
ndims = 3
class Array4DExtensionType(_ArrayXDExtensionType):
ndims = 4
class Array5DExtensionType(_ArrayXDExtensionType):
ndims = 5
def _is_zero_copy_only(pa_type: pa.DataType) -> bool:
"""
When converting a pyarrow array to a numpy array, we must know whether this could be done in zero-copy or not.
This function returns the value of the ``zero_copy_only`` parameter to pass to ``.to_numpy()``, given the type of the pyarrow array.
# zero copy is available for all primitive types except booleans
# primitive types are types for which the physical representation in arrow and in numpy
# https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821
# see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy
# and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22
"""
return is_primitive(pa_type) and not is_boolean(pa_type)
class ArrayExtensionArray(pa.ExtensionArray):
def __array__(self):
zero_copy_only = _is_zero_copy_only(self.storage.type)
return self.to_numpy(zero_copy_only=zero_copy_only)
def __getitem__(self, i):
return self.storage[i]
def to_numpy(self, zero_copy_only=True):
storage: pa.ListArray = self.storage
size = 1
for i in range(self.type.ndims):
size *= self.type.shape[i]
storage = storage.flatten()
numpy_arr = storage.to_numpy(zero_copy_only=zero_copy_only)
numpy_arr = numpy_arr.reshape(len(self), *self.type.shape)
return numpy_arr
def to_pylist(self):
zero_copy_only = _is_zero_copy_only(self.storage.type)
return self.to_numpy(zero_copy_only=zero_copy_only).tolist()
class PandasArrayExtensionDtype(PandasExtensionDtype):
_metadata = "value_type"
def __init__(self, value_type: Union["PandasArrayExtensionDtype", np.dtype]):
self._value_type = value_type
def __from_arrow__(self, array):
zero_copy_only = _is_zero_copy_only(array.type)
if isinstance(array, pa.ChunkedArray):
numpy_arr = np.vstack([chunk.to_numpy(zero_copy_only=zero_copy_only) for chunk in array.chunks])
else:
numpy_arr = array.to_numpy(zero_copy_only=zero_copy_only)
return PandasArrayExtensionArray(numpy_arr)
@classmethod
def construct_array_type(cls):
return PandasArrayExtensionArray
@property
def type(self) -> type:
return np.ndarray
@property
def kind(self) -> str:
return "O"
@property
def name(self) -> str:
return f"array[{self.value_type}]"
@property
def value_type(self) -> np.dtype:
return self._value_type
class PandasArrayExtensionArray(PandasExtensionArray):
def __init__(self, data: np.ndarray, copy: bool = False):
self._data = data if not copy else np.array(data)
self._dtype = PandasArrayExtensionDtype(data.dtype)
def __array__(self, dtype=None):
"""
Convert to NumPy Array.
Note that Pandas expects a 1D array when dtype is set to object.
But for other dtypes, the returned shape is the same as the one of ``data``.
More info about pandas 1D requirement for PandasExtensionArray here:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray
"""
if dtype == object:
out = np.empty(len(self._data), dtype=object)
for i in range(len(self._data)):
out[i] = self._data[i]
return out
if dtype is None:
return self._data
else:
return self._data.astype(dtype)
def copy(self, deep: bool = False) -> "PandasArrayExtensionArray":
return PandasArrayExtensionArray(self._data, copy=True)
@classmethod
def _from_sequence(
cls, scalars, dtype: Optional[PandasArrayExtensionDtype] = None, copy: bool = False
) -> "PandasArrayExtensionArray":
data = np.array(scalars, dtype=dtype if dtype is None else dtype.value_type, copy=copy)
return cls(data, copy=copy)
@classmethod
def _concat_same_type(cls, to_concat: Sequence_["PandasArrayExtensionArray"]) -> "PandasArrayExtensionArray":
data = np.vstack([va._data for va in to_concat])
return cls(data, copy=False)
@property
def dtype(self) -> PandasArrayExtensionDtype:
return self._dtype
@property
def nbytes(self) -> int:
return self._data.nbytes
def isna(self) -> np.ndarray:
return np.array([pd.isna(arr).any() for arr in self._data])
def __setitem__(self, key: Union[int, slice, np.ndarray], value: Any) -> None:
raise NotImplementedError()
def __getitem__(self, item: Union[int, slice, np.ndarray]) -> Union[np.ndarray, "PandasArrayExtensionArray"]:
if isinstance(item, int):
return self._data[item]
return PandasArrayExtensionArray(self._data[item], copy=False)
def take(
self, indices: Sequence_[int], allow_fill: bool = False, fill_value: bool = None
) -> "PandasArrayExtensionArray":
indices: np.ndarray = np.asarray(indices, dtype="int")
if allow_fill:
fill_value = (
self.dtype.na_value if fill_value is None else np.asarray(fill_value, dtype=self.dtype.value_type)
)
mask = indices == -1
if (indices < -1).any():
raise ValueError("Invalid value in `indices`, must be all >= -1 for `allow_fill` is True")
elif len(self) > 0:
pass
elif not np.all(mask):
raise IndexError("Invalid take for empty PandasArrayExtensionArray, must be all -1.")
else:
data = np.array([fill_value] * len(indices), dtype=self.dtype.value_type)
return PandasArrayExtensionArray(data, copy=False)
took = self._data.take(indices, axis=0)
if allow_fill and mask.any():
took[mask] = [fill_value] * np.sum(mask)
return PandasArrayExtensionArray(took, copy=False)
def __len__(self) -> int:
return len(self._data)
def __eq__(self, other) -> np.ndarray:
if not isinstance(other, PandasArrayExtensionArray):
raise NotImplementedError("Invalid type to compare to: {}".format(type(other)))
return (self._data == other._data).all()
def pandas_types_mapper(dtype):
if isinstance(dtype, _ArrayXDExtensionType):
return PandasArrayExtensionDtype(dtype.value_type)
[docs]@dataclass
class ClassLabel:
"""Handle integer class labels. Here for compatiblity with tfds.
There are 3 ways to define a ClassLabel, which correspond to the 3
arguments:
* `num_classes`: create 0 to (num_classes-1) labels
* `names`: a list of label strings
* `names_file`: a file containing the list of labels.
Note: On python2, the strings are encoded as utf-8.
Args:
num_classes: `int`, number of classes. All labels must be < num_classes.
names: `list<str>`, string names for the integer classes. The
order in which the names are provided is kept.
names_file: `str`, path to a file with names for the integer
classes, one per line.
"""
num_classes: int = None
names: List[str] = None
names_file: Optional[str] = None
id: Optional[str] = None
# Automatically constructed
dtype: ClassVar[str] = "int64"
pa_type: ClassVar[Any] = pa.int64()
_str2int: ClassVar[Dict[str, int]] = None
_int2str: ClassVar[Dict[int, int]] = None
_type: str = field(default="ClassLabel", init=False, repr=False)
def __post_init__(self):
if self.names_file is not None and self.names is not None:
raise ValueError("Please provide either names or names_file but not both.")
# Set self.names
if self.names is None:
if self.names_file is not None:
self.names = self._load_names_from_file(self.names_file)
elif self.num_classes is not None:
self.names = [str(i) for i in range(self.num_classes)]
else:
raise ValueError("Please provide either num_classes, names or names_file.")
# Set self.num_classes
if self.num_classes is None:
self.num_classes = len(self.names)
elif self.num_classes != len(self.names):
raise ValueError(
"ClassLabel number of names do not match the defined num_classes. "
"Got {} names VS {} num_classes".format(len(self.names), self.num_classes)
)
# Prepare mappings
self._int2str = [str(name) for name in self.names]
self._str2int = {name: i for i, name in enumerate(self._int2str)}
if len(self._int2str) != len(self._str2int):
raise ValueError("Some label names are duplicated. Each label name should be unique.")
def __call__(self):
return self.pa_type
[docs] def str2int(self, values: Union[str, Iterable]):
"""Conversion class name string => integer."""
assert isinstance(values, str) or isinstance(
values, Iterable
), f"Values {values} should be a string or an Iterable (list, numpy array, pytorch, tensorflow tensors)"
return_list = True
if isinstance(values, str):
values = [values]
return_list = False
output = []
for value in values:
if self._str2int:
# strip key if not in dict
if value not in self._str2int:
value = str(value).strip()
output.append(self._str2int[str(value)])
else:
# No names provided, try to integerize
failed_parse = False
try:
output.append(int(value))
except ValueError:
failed_parse = True
if failed_parse or not 0 <= value < self.num_classes:
raise ValueError("Invalid string class label %s" % value)
return output if return_list else output[0]
[docs] def int2str(self, values: Union[int, Iterable]):
"""Conversion integer => class name string."""
assert isinstance(values, int) or isinstance(
values, Iterable
), f"Values {values} should be an integer or an Iterable (list, numpy array, pytorch, tensorflow tensors)"
return_list = True
if isinstance(values, int):
values = [values]
return_list = False
for v in values:
if not 0 <= v < self.num_classes:
raise ValueError("Invalid integer class label %d" % v)
if self._int2str:
output = [self._int2str[int(v)] for v in values]
else:
# No names provided, return str(values)
output = [str(v) for v in values]
return output if return_list else output[0]
def encode_example(self, example_data):
if self.num_classes is None:
raise ValueError(
"Trying to use ClassLabel feature with undefined number of class. "
"Please set ClassLabel.names or num_classes."
)
# If a string is given, convert to associated integer
if isinstance(example_data, str):
example_data = self.str2int(example_data)
# Allowing -1 to mean no label.
if not -1 <= example_data < self.num_classes:
raise ValueError(
"Class label %d greater than configured num_classes %d" % (example_data, self.num_classes)
)
return example_data
@staticmethod
def _load_names_from_file(names_filepath):
with open(names_filepath, "r", encoding="utf-8") as f:
return [name.strip() for name in f.read().split("\n") if name.strip()] # Filter empty names
[docs]@dataclass
class Translation:
"""`FeatureConnector` for translations with fixed languages per example.
Here for compatiblity with tfds.
Input: The Translate feature accepts a dictionary for each example mapping
string language codes to string translations.
Output: A dictionary mapping string language codes to translations as `Text`
features.
Example::
# At construction time:
datasets.features.Translation(languages=['en', 'fr', 'de'])
# During data generation:
yield {
'en': 'the cat',
'fr': 'le chat',
'de': 'die katze'
}
"""
languages: List[str]
id: Optional[str] = None
# Automatically constructed
dtype: ClassVar[str] = "dict"
pa_type: ClassVar[Any] = None
_type: str = field(default="Translation", init=False, repr=False)
def __call__(self):
return pa.struct({lang: pa.string() for lang in sorted(self.languages)})
[docs]@dataclass
class TranslationVariableLanguages:
"""`FeatureConnector` for translations with variable languages per example.
Here for compatiblity with tfds.
Input: The TranslationVariableLanguages feature accepts a dictionary for each
example mapping string language codes to one or more string translations.
The languages present may vary from example to example.
Output:
language: variable-length 1D tf.Tensor of tf.string language codes, sorted
in ascending order.
translation: variable-length 1D tf.Tensor of tf.string plain text
translations, sorted to align with language codes.
Example::
# At construction time:
datasets.features.Translation(languages=['en', 'fr', 'de'])
# During data generation:
yield {
'en': 'the cat',
'fr': ['le chat', 'la chatte,']
'de': 'die katze'
}
# Tensor returned :
{
'language': ['en', 'de', 'fr', 'fr'],
'translation': ['the cat', 'die katze', 'la chatte', 'le chat'],
}
"""
languages: Optional[List] = None
num_languages: Optional[int] = None
id: Optional[str] = None
# Automatically constructed
dtype: ClassVar[str] = "dict"
pa_type: ClassVar[Any] = None
_type: str = field(default="TranslationVariableLanguages", init=False, repr=False)
def __post_init__(self):
self.languages = list(sorted(list(set(self.languages)))) if self.languages else None
self.num_languages = len(self.languages) if self.languages else None
def __call__(self):
return pa.struct({"language": pa.list_(pa.string()), "translation": pa.list_(pa.string())})
def encode_example(self, translation_dict):
lang_set = set(self.languages)
if self.languages and set(translation_dict) - lang_set:
raise ValueError(
"Some languages in example ({0}) are not in valid set ({1}).".format(
", ".join(sorted(set(translation_dict) - lang_set)), ", ".join(lang_set)
)
)
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
translation_tuples = []
for lang, text in translation_dict.items():
if isinstance(text, str):
translation_tuples.append((lang, text))
else:
translation_tuples.extend([(lang, el) for el in text])
# Ensure translations are in ascending order by language code.
languages, translations = zip(*sorted(translation_tuples))
return {"language": languages, "translation": translations}
[docs]@dataclass
class Sequence:
"""Construct a list of feature from a single type or a dict of types.
Mostly here for compatiblity with tfds.
"""
feature: Any
length: int = -1
id: Optional[str] = None
# Automatically constructed
dtype: ClassVar[str] = "list"
pa_type: ClassVar[Any] = None
_type: str = field(default="Sequence", init=False, repr=False)
FeatureType = Union[
dict,
list,
tuple,
Value,
ClassLabel,
Translation,
TranslationVariableLanguages,
Sequence,
Array2D,
Array3D,
Array4D,
Array5D,
]
def get_nested_type(schema: FeatureType) -> pa.DataType:
"""
get_nested_type() converts a datasets.FeatureType into a pyarrow.DataType, and acts as the inverse of
generate_from_arrow_type().
It performs double-duty as the implementation of Features.type and handles the conversion of
datasets.Feature->pa.struct
"""
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, Features):
return pa.struct(
{key: get_nested_type(schema[key]) for key in schema}
) # Features is subclass of dict, and dict order is deterministic since Python 3.6
elif isinstance(schema, dict):
return pa.struct(
{key: get_nested_type(schema[key]) for key in schema}
) # however don't sort on struct types since the order matters
elif isinstance(schema, (list, tuple)):
assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type"
value_type = get_nested_type(schema[0])
return pa.list_(value_type)
elif isinstance(schema, Sequence):
value_type = get_nested_type(schema.feature)
# We allow to reverse list of dict => dict of list for compatibility with tfds
if isinstance(value_type, pa.StructType):
return pa.struct({f.name: pa.list_(f.type, schema.length) for f in value_type})
return pa.list_(value_type, schema.length)
# Other objects are callable which returns their data type (ClassLabel, Array2D, Translation, Arrow datatype creation methods)
return schema()
def encode_nested_example(schema, obj):
"""Encode a nested example.
This is used since some features (in particular ClassLabel) have some logic during encoding.
"""
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, dict):
return {
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
}
elif isinstance(schema, (list, tuple)):
sub_schema = schema[0]
return [encode_nested_example(sub_schema, o) for o in obj] if obj is not None else None
elif isinstance(schema, Sequence):
# We allow to reverse list of dict => dict of list for compatiblity with tfds
if isinstance(schema.feature, dict):
# dict of list to fill
list_dict = {}
if isinstance(obj, (list, tuple)):
# obj is a list of dict
for k, dict_tuples in utils.zip_dict(schema.feature, *obj):
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
return list_dict
else:
# obj is a single dict
for k, (sub_schema, sub_objs) in utils.zip_dict(schema.feature, obj):
list_dict[k] = [encode_nested_example(sub_schema, o) for o in sub_objs]
return list_dict
# schema.feature is not a dict
if isinstance(obj, str): # don't interpret a string as a list
raise ValueError("Got a string but expected a list instead: '{}'".format(obj))
return [encode_nested_example(schema.feature, o) for o in obj] if obj is not None else None
# Object with special encoding:
# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
return schema.encode_example(obj)
# Other object should be directly convertible to a native Arrow type (like Translation and Translation)
return obj
def generate_from_dict(obj: Any):
"""Regenerate the nested feature object from a deserialized dict.
We use the '_type' fields to get the dataclass name to load.
generate_from_dict is the recursive helper for Features.from_dict, and allows for a convenient constructor syntax
to define features from deserialized JSON dictionaries. This function is used in particular when deserializing
a :class:`DatasetInfo` that was dumped to a JSON object. This acts as an analogue to
:meth:`Features.from_arrow_schema` and handles the recursive field-by-field instantiation, but doesn't require any
mapping to/from pyarrow, except for the fact that it takes advantage of the mapping of pyarrow primitive dtypes
that :class:`Value` automatically performs.
"""
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(obj, list):
return [generate_from_dict(value) for value in obj]
# Otherwise we have a dict or a dataclass
if "_type" not in obj or isinstance(obj["_type"], dict):
return {key: generate_from_dict(value) for key, value in obj.items()}
class_type = globals()[obj.pop("_type")]
if class_type == Sequence:
return Sequence(feature=generate_from_dict(obj["feature"]), length=obj["length"])
field_names = set(f.name for f in fields(class_type))
return class_type(**{k: v for k, v in obj.items() if k in field_names})
def generate_from_arrow_type(pa_type: pa.DataType) -> FeatureType:
"""
generate_from_arrow_type accepts an arrow DataType and returns a datasets FeatureType to be used as the type for
a single field.
This is the high-level arrow->datasets type conversion and is inverted by get_nested_type().
This operates at the individual *field* level, whereas Features.from_arrow_schema() operates at the
full schema level and holds the methods that represent the bijection from Features<->pyarrow.Schema
"""
if isinstance(pa_type, pa.StructType):
return {field.name: generate_from_arrow_type(field.type) for field in pa_type}
elif isinstance(pa_type, pa.FixedSizeListType):
return Sequence(feature=generate_from_arrow_type(pa_type.value_type), length=pa_type.list_size)
elif isinstance(pa_type, pa.ListType):
feature = generate_from_arrow_type(pa_type.value_type)
if isinstance(feature, (dict, tuple, list)):
return [feature]
return Sequence(feature=feature)
elif isinstance(pa_type, _ArrayXDExtensionType):
array_feature = [None, None, Array2D, Array3D, Array4D, Array5D][pa_type.ndims]
return array_feature(shape=pa_type.shape, dtype=pa_type.value_type)
elif isinstance(pa_type, pa.DictionaryType):
raise NotImplementedError # TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table
elif isinstance(pa_type, pa.DataType):
return Value(dtype=_arrow_to_datasets_dtype(pa_type))
else:
raise ValueError(f"Cannot convert {pa_type} to a Feature type.")
def numpy_to_pyarrow_listarray(arr: np.ndarray, type: pa.DataType = None) -> pa.ListArray:
"""Build a PyArrow ListArray from a multidimensional NumPy array"""
values = pa.array(arr.flatten(), type=type)
for i in range(arr.ndim - 1):
n_offsets = reduce(mul, arr.shape[: arr.ndim - i - 1], 1)
step_offsets = arr.shape[arr.ndim - i - 1]
offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32())
values = pa.ListArray.from_arrays(offsets, values)
return values
[docs]class Features(dict):
@property
def type(self):
"""
Features field types.
Returns:
:obj:`pyarrow.DataType`
"""
return get_nested_type(self)
[docs] @classmethod
def from_arrow_schema(cls, pa_schema: pa.Schema) -> "Features":
"""
Construct Features from Arrow Schema.
Args:
pa_schema (:obj:`pyarrow.Schema`): Arrow Schema.
Returns:
:class:`Features`
"""
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
return cls(**obj)
[docs] @classmethod
def from_dict(cls, dic) -> "Features":
"""
Construct Features from dict.
Regenerate the nested feature object from a deserialized dict.
We use the '_type' key to infer the dataclass name of the feature FieldType.
It allows for a convenient constructor syntax
to define features from deserialized JSON dictionaries. This function is used in particular when deserializing
a :class:`DatasetInfo` that was dumped to a JSON object. This acts as an analogue to
:meth:`Features.from_arrow_schema` and handles the recursive field-by-field instantiation, but doesn't require
any mapping to/from pyarrow, except for the fact that it takes advantage of the mapping of pyarrow primitive
dtypes that :class:`Value` automatically performs.
Args:
dic (:obj:`dict[str, Any]`): Python dictionary.
Returns:
:class:`Features`
Examples:
>>> Features.from_dict({'_type': {'dtype': 'string', 'id': None, '_type': 'Value'}})
{'_type': Value(dtype='string', id=None)}
"""
obj = generate_from_dict(dic)
return cls(**obj)
[docs] def encode_example(self, example):
"""
Encode example into a format for Arrow.
Args:
example (:obj:`dict[str, Any]`): Data in a Dataset row.
Returns:
:obj:`dict[str, Any]`
"""
example = cast_to_python_objects(example)
return encode_nested_example(self, example)
[docs] def encode_batch(self, batch):
"""
Encode batch into a format for Arrow.
Args:
batch (:obj:`dict[str, list[Any]]`): Data in a Dataset batch.
Returns:
:obj:`dict[str, list[Any]]`
"""
encoded_batch = {}
if set(batch) != set(self):
raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
for key, column in batch.items():
column = cast_to_python_objects(column)
encoded_batch[key] = [encode_nested_example(self[key], obj) for obj in column]
return encoded_batch
[docs] def copy(self) -> "Features":
"""
Make a deep copy of Features.
Returns:
:class:`Features`
"""
return copy.deepcopy(self)
[docs] def reorder_fields_as(self, other: "Features") -> "Features":
"""
Reorder Features fields to match the field order of other Features.
The order of the fields is important since it matters for the underlying arrow data.
Re-ordering the fields allows to make the underlying arrow data type match.
Args:
other (:class:`Features`): The other Features to align with.
Returns:
:class:`Features`
Examples:
>>> from datasets import Features, Sequence, Value
>>> # let's say we have to features with a different order of nested fields (for a and b for example)
>>> f1 = Features({"root": Sequence({"a": Value("string"), "b": Value("string")})})
>>> f2 = Features({"root": {"b": Sequence(Value("string")), "a": Sequence(Value("string"))}})
>>> assert f1.type != f2.type
>>> # re-ordering keeps the base structure (here Sequence is defined at the root level), but make the fields order match
>>> f1.reorder_fields_as(f2)
{'root': Sequence(feature={'b': Value(dtype='string', id=None), 'a': Value(dtype='string', id=None)}, length=-1, id=None)}
>>> assert f1.reorder_fields_as(f2).type == f2.type
"""
def recursive_reorder(source, target, stack=""):
stack_position = " at " + stack[1:] if stack else ""
if isinstance(target, Sequence):
target = target.feature
if isinstance(target, dict):
target = {k: [v] for k, v in target.items()}
else:
target = [target]
if isinstance(source, Sequence):
source, id_, length = source.feature, source.id, source.length
if isinstance(source, dict):
source = {k: [v] for k, v in source.items()}
reordered = recursive_reorder(source, target, stack)
return Sequence({k: v[0] for k, v in reordered.items()}, id=id_, length=length)
else:
source = [source]
reordered = recursive_reorder(source, target, stack)
return Sequence(reordered[0], id=id_, length=length)
elif isinstance(source, dict):
if not isinstance(target, dict):
raise ValueError(f"Type mismatch: between {source} and {target}" + stack_position)
if sorted(source) != sorted(target):
raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
return {key: recursive_reorder(source[key], target[key], stack + f".{key}") for key in target}
elif isinstance(source, list):
if not isinstance(target, list):
raise ValueError(f"Type mismatch: between {source} and {target}" + stack_position)
if len(source) != len(target):
raise ValueError(f"Length mismatch: between {source} and {target}" + stack_position)
return [recursive_reorder(source[i], target[i], stack + f".<list>") for i in range(len(target))]
else:
return source
return Features(recursive_reorder(self, other))