diff --git "a/llmeval-env/lib/python3.10/site-packages/datasets/arrow_dataset.py" "b/llmeval-env/lib/python3.10/site-packages/datasets/arrow_dataset.py"
new file mode 100644--- /dev/null
+++ "b/llmeval-env/lib/python3.10/site-packages/datasets/arrow_dataset.py"
@@ -0,0 +1,6495 @@
+# Copyright 2020 The HuggingFace 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
+"""Simple Dataset wrapping an Arrow Table."""
+
+import contextlib
+import copy
+import fnmatch
+import itertools
+import json
+import math
+import os
+import posixpath
+import re
+import shutil
+import sys
+import tempfile
+import time
+import warnings
+import weakref
+from collections import Counter
+from collections.abc import Mapping
+from copy import deepcopy
+from functools import partial, wraps
+from io import BytesIO
+from math import ceil, floor
+from pathlib import Path
+from random import sample
+from typing import (
+ TYPE_CHECKING,
+ Any,
+ BinaryIO,
+ Callable,
+ Dict,
+ Iterable,
+ Iterator,
+ List,
+ Optional,
+ Tuple,
+ Union,
+ overload,
+)
+from typing import Sequence as Sequence_
+
+import fsspec
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.compute as pc
+from fsspec.core import url_to_fs
+from huggingface_hub import (
+ CommitInfo,
+ CommitOperationAdd,
+ CommitOperationDelete,
+ DatasetCard,
+ DatasetCardData,
+ HfApi,
+)
+from huggingface_hub.hf_api import RepoFile
+from multiprocess import Pool
+from tqdm.contrib.concurrent import thread_map
+
+from . import config
+from .arrow_reader import ArrowReader
+from .arrow_writer import ArrowWriter, OptimizedTypedSequence
+from .data_files import sanitize_patterns
+from .download.streaming_download_manager import xgetsize
+from .features import Audio, ClassLabel, Features, Image, Sequence, Value
+from .features.features import (
+ FeatureType,
+ _align_features,
+ _check_if_features_can_be_aligned,
+ generate_from_arrow_type,
+ pandas_types_mapper,
+ require_decoding,
+)
+from .filesystems import is_remote_filesystem
+from .fingerprint import (
+ fingerprint_transform,
+ format_kwargs_for_fingerprint,
+ format_transform_for_fingerprint,
+ generate_fingerprint,
+ generate_random_fingerprint,
+ get_temporary_cache_files_directory,
+ is_caching_enabled,
+ maybe_register_dataset_for_temp_dir_deletion,
+ update_fingerprint,
+ validate_fingerprint,
+)
+from .formatting import format_table, get_format_type_from_alias, get_formatter, query_table
+from .formatting.formatting import LazyDict, _is_range_contiguous
+from .info import DatasetInfo, DatasetInfosDict
+from .naming import _split_re
+from .search import IndexableMixin
+from .splits import NamedSplit, Split, SplitDict, SplitInfo
+from .table import (
+ InMemoryTable,
+ MemoryMappedTable,
+ Table,
+ _memory_mapped_record_batch_reader_from_file,
+ cast_array_to_feature,
+ concat_tables,
+ embed_table_storage,
+ list_table_cache_files,
+ table_cast,
+ table_iter,
+ table_visitor,
+)
+from .tasks import TaskTemplate
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+from .utils.deprecation_utils import deprecated
+from .utils.file_utils import estimate_dataset_size
+from .utils.info_utils import is_small_dataset
+from .utils.metadata import MetadataConfigs
+from .utils.py_utils import (
+ Literal,
+ asdict,
+ convert_file_size_to_int,
+ glob_pattern_to_regex,
+ iflatmap_unordered,
+ string_to_dict,
+ unique_values,
+)
+from .utils.stratify import stratified_shuffle_split_generate_indices
+from .utils.tf_utils import dataset_to_tf, minimal_tf_collate_fn, multiprocess_dataset_to_tf
+from .utils.typing import ListLike, PathLike
+
+
+if TYPE_CHECKING:
+ import sqlite3
+
+ import polars as pl
+ import pyspark
+ import sqlalchemy
+
+ from .dataset_dict import DatasetDict
+ from .iterable_dataset import IterableDataset
+
+logger = logging.get_logger(__name__)
+
+PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED = (
+ "data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.parquet"
+)
+
+
+class DatasetInfoMixin:
+ """This base class exposes some attributes of DatasetInfo
+ at the base level of the Dataset for easy access.
+ """
+
+ def __init__(self, info: DatasetInfo, split: Optional[NamedSplit]):
+ self._info = info
+ self._split = split
+
+ @property
+ def info(self):
+ """[`~datasets.DatasetInfo`] object containing all the metadata in the dataset."""
+ return self._info
+
+ @property
+ def split(self):
+ """[`~datasets.NamedSplit`] object corresponding to a named dataset split."""
+ return self._split
+
+ @property
+ def builder_name(self) -> str:
+ return self._info.builder_name
+
+ @property
+ def citation(self) -> str:
+ return self._info.citation
+
+ @property
+ def config_name(self) -> str:
+ return self._info.config_name
+
+ @property
+ def dataset_size(self) -> Optional[int]:
+ return self._info.dataset_size
+
+ @property
+ def description(self) -> str:
+ return self._info.description
+
+ @property
+ def download_checksums(self) -> Optional[dict]:
+ return self._info.download_checksums
+
+ @property
+ def download_size(self) -> Optional[int]:
+ return self._info.download_size
+
+ @property
+ def features(self) -> Optional[Features]:
+ return self._info.features.copy() if self._info.features is not None else None
+
+ @property
+ def homepage(self) -> Optional[str]:
+ return self._info.homepage
+
+ @property
+ def license(self) -> Optional[str]:
+ return self._info.license
+
+ @property
+ def size_in_bytes(self) -> Optional[int]:
+ return self._info.size_in_bytes
+
+ @property
+ def supervised_keys(self):
+ return self._info.supervised_keys
+
+ @property
+ def task_templates(self):
+ return self._info.task_templates
+
+ @property
+ def version(self):
+ return self._info.version
+
+
+class TensorflowDatasetMixin:
+ _TF_DATASET_REFS = set()
+
+ @staticmethod
+ def _get_output_signature(
+ dataset: "Dataset",
+ collate_fn: Callable,
+ collate_fn_args: dict,
+ cols_to_retain: Optional[List[str]] = None,
+ batch_size: Optional[int] = None,
+ num_test_batches: int = 20,
+ ):
+ """Private method used by `to_tf_dataset()` to find the shapes and dtypes of samples from this dataset
+ after being passed through the collate_fn. Tensorflow needs an exact signature for tf.numpy_function, so
+ the only way to do this is to run test batches - the collator may add or rename columns, so we can't figure
+ it out just by inspecting the dataset.
+
+ Args:
+ dataset (`Dataset`): Dataset to load samples from.
+ collate_fn(`bool`): Shuffle the dataset order when loading. Recommended True for training, False for
+ validation/evaluation.
+ collate_fn(`Callable`): A function or callable object (such as a `DataCollator`) that will collate
+ lists of samples into a batch.
+ collate_fn_args (`Dict`): A `dict` of keyword arguments to be passed to the
+ `collate_fn`.
+ batch_size (`int`, optional): The size of batches loaded from the dataset. Used for shape inference.
+ Can be None, which indicates that batch sizes can be variable.
+ num_test_batches (`int`): The number of batches to load from the dataset for shape inference.
+
+ Returns:
+ `dict`: Dict mapping column names to tf.Tensorspec objects
+ `dict`: Dict mapping column names to np.dtype objects
+ """
+ if config.TF_AVAILABLE:
+ import tensorflow as tf
+ else:
+ raise ImportError("Called a Tensorflow-specific function but Tensorflow is not installed.")
+
+ if len(dataset) == 0:
+ raise ValueError("Unable to get the output signature because the dataset is empty.")
+ if batch_size is not None:
+ batch_size = min(len(dataset), batch_size)
+ test_batch_size = 1
+
+ if cols_to_retain is not None:
+ cols_to_retain = list(set(cols_to_retain + ["label_ids", "label", "labels"]))
+
+ test_batches = []
+ for _ in range(num_test_batches):
+ indices = sample(range(len(dataset)), test_batch_size)
+ test_batch = dataset[indices]
+ if cols_to_retain is not None:
+ test_batch = {key: value for key, value in test_batch.items() if key in cols_to_retain}
+ test_batch = [{key: value[i] for key, value in test_batch.items()} for i in range(test_batch_size)]
+ test_batch = collate_fn(test_batch, **collate_fn_args)
+ test_batches.append(test_batch)
+
+ tf_columns_to_signatures = {}
+ np_columns_to_dtypes = {}
+ for column in test_batches[0].keys():
+ raw_arrays = [batch[column] for batch in test_batches]
+ # In case the collate_fn returns something strange
+ np_arrays = []
+ for array in raw_arrays:
+ if isinstance(array, np.ndarray):
+ np_arrays.append(array)
+ elif isinstance(array, tf.Tensor):
+ np_arrays.append(array.numpy())
+ else:
+ np_arrays.append(np.array(array))
+
+ if np.issubdtype(np_arrays[0].dtype, np.integer) or np_arrays[0].dtype == bool:
+ tf_dtype = tf.int64
+ np_dtype = np.int64
+ elif np.issubdtype(np_arrays[0].dtype, np.number):
+ tf_dtype = tf.float32
+ np_dtype = np.float32
+ elif np_arrays[0].dtype.kind == "U": # Unicode strings
+ np_dtype = np.unicode_
+ tf_dtype = tf.string
+ else:
+ raise RuntimeError(
+ f"Unrecognized array dtype {np_arrays[0].dtype}. \n"
+ "Nested types and image/audio types are not supported yet."
+ )
+ shapes = [array.shape for array in np_arrays]
+ static_shape = []
+ for dim in range(len(shapes[0])):
+ sizes = {shape[dim] for shape in shapes}
+ if dim == 0:
+ static_shape.append(batch_size)
+ continue
+ if len(sizes) == 1: # This dimension looks constant
+ static_shape.append(sizes.pop())
+ else: # Use None for variable dimensions
+ static_shape.append(None)
+ tf_columns_to_signatures[column] = tf.TensorSpec(shape=static_shape, dtype=tf_dtype)
+ np_columns_to_dtypes[column] = np_dtype
+
+ return tf_columns_to_signatures, np_columns_to_dtypes
+
+ def to_tf_dataset(
+ self,
+ batch_size: Optional[int] = None,
+ columns: Optional[Union[str, List[str]]] = None,
+ shuffle: bool = False,
+ collate_fn: Optional[Callable] = None,
+ drop_remainder: bool = False,
+ collate_fn_args: Optional[Dict[str, Any]] = None,
+ label_cols: Optional[Union[str, List[str]]] = None,
+ prefetch: bool = True,
+ num_workers: int = 0,
+ num_test_batches: int = 20,
+ ):
+ """Create a `tf.data.Dataset` from the underlying Dataset. This `tf.data.Dataset` will load and collate batches from
+ the Dataset, and is suitable for passing to methods like `model.fit()` or `model.predict()`. The dataset will yield
+ `dicts` for both inputs and labels unless the `dict` would contain only a single key, in which case a raw
+ `tf.Tensor` is yielded instead.
+
+ Args:
+ batch_size (`int`, *optional*):
+ Size of batches to load from the dataset. Defaults to `None`, which implies that the dataset won't be
+ batched, but the returned dataset can be batched later with `tf_dataset.batch(batch_size)`.
+ columns (`List[str]` or `str`, *optional*):
+ Dataset column(s) to load in the `tf.data.Dataset`.
+ Column names that are created by the `collate_fn` and that do not exist in the original dataset can be used.
+ shuffle(`bool`, defaults to `False`):
+ Shuffle the dataset order when loading. Recommended `True` for training, `False` for
+ validation/evaluation.
+ drop_remainder(`bool`, defaults to `False`):
+ Drop the last incomplete batch when loading. Ensures
+ that all batches yielded by the dataset will have the same length on the batch dimension.
+ collate_fn(`Callable`, *optional*):
+ A function or callable object (such as a `DataCollator`) that will collate
+ lists of samples into a batch.
+ collate_fn_args (`Dict`, *optional*):
+ An optional `dict` of keyword arguments to be passed to the
+ `collate_fn`.
+ label_cols (`List[str]` or `str`, defaults to `None`):
+ Dataset column(s) to load as labels.
+ Note that many models compute loss internally rather than letting Keras do it, in which case
+ passing the labels here is optional, as long as they're in the input `columns`.
+ prefetch (`bool`, defaults to `True`):
+ Whether to run the dataloader in a separate thread and maintain
+ a small buffer of batches for training. Improves performance by allowing data to be loaded in the
+ background while the model is training.
+ num_workers (`int`, defaults to `0`):
+ Number of workers to use for loading the dataset. Only supported on Python versions >= 3.8.
+ num_test_batches (`int`, defaults to `20`):
+ Number of batches to use to infer the output signature of the dataset.
+ The higher this number, the more accurate the signature will be, but the longer it will take to
+ create the dataset.
+
+ Returns:
+ `tf.data.Dataset`
+
+ Example:
+
+ ```py
+ >>> ds_train = ds["train"].to_tf_dataset(
+ ... columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],
+ ... shuffle=True,
+ ... batch_size=16,
+ ... collate_fn=data_collator,
+ ... )
+ ```
+ """
+ if config.TF_AVAILABLE:
+ import tensorflow as tf
+ else:
+ raise ImportError("Called a Tensorflow-specific function but Tensorflow is not installed.")
+
+ if (isinstance(columns, list) and len(columns) == 1) or (
+ isinstance(label_cols, list) and len(label_cols) == 1
+ ):
+ warnings.warn(
+ "The output of `to_tf_dataset` will change when a passing single element list for `labels` or "
+ "`columns` in the next datasets version. To return a tuple structure rather than dict, pass a "
+ "single string.\n"
+ "Old behaviour: columns=['a'], labels=['labels'] -> (tf.Tensor, tf.Tensor) \n"
+ " : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n"
+ "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor}) \n"
+ " : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) ",
+ FutureWarning,
+ )
+
+ if isinstance(tf.distribute.get_strategy(), tf.distribute.TPUStrategy):
+ logger.warning(
+ "Note that to_tf_dataset() loads the data with a generator rather than a full tf.data "
+ "pipeline and is not compatible with remote TPU connections. If you encounter errors, please "
+ "try using a TPU VM or, if your data can fit in memory, loading it into memory as a dict of "
+ "Tensors instead of streaming with to_tf_dataset()."
+ )
+
+ if collate_fn is None:
+ # Set a very simple default collator that just stacks things together
+ collate_fn = minimal_tf_collate_fn
+ if collate_fn_args is None:
+ collate_fn_args = {}
+ if label_cols and not columns:
+ raise ValueError("Cannot specify label_cols without specifying columns!")
+ if label_cols is None:
+ label_cols = []
+ elif isinstance(label_cols, str):
+ label_cols = [label_cols]
+ if len(set(label_cols)) < len(label_cols):
+ raise ValueError("List of label_cols contains duplicates.")
+ if columns:
+ if isinstance(columns, str):
+ columns = [columns]
+ if len(set(columns)) < len(columns):
+ raise ValueError("List of columns contains duplicates.")
+ cols_to_retain = list(set(columns + label_cols))
+ else:
+ cols_to_retain = None # Indicates keeping all valid columns
+ columns = []
+
+ if self.format["type"] not in ["custom", "numpy"]:
+ dataset = self.with_format("numpy")
+ else:
+ dataset = self
+
+ # TODO(Matt, QL): deprecate the retention of label_ids and label
+
+ output_signature, columns_to_np_types = dataset._get_output_signature(
+ dataset,
+ collate_fn=collate_fn,
+ collate_fn_args=collate_fn_args,
+ cols_to_retain=cols_to_retain,
+ batch_size=batch_size if drop_remainder else None,
+ num_test_batches=num_test_batches,
+ )
+
+ if "labels" in output_signature:
+ if ("label_ids" in columns or "label" in columns) and "labels" not in columns:
+ columns = [col for col in columns if col not in ["label_ids", "label"]] + ["labels"]
+ if ("label_ids" in label_cols or "label" in label_cols) and "labels" not in label_cols:
+ label_cols = [col for col in label_cols if col not in ["label_ids", "label"]] + ["labels"]
+
+ for col in columns:
+ if col not in output_signature:
+ raise ValueError(f"Column {col} not found in dataset!")
+
+ for col in label_cols:
+ if col not in output_signature:
+ raise ValueError(f"Label column {col} not found in dataset!")
+
+ if num_workers == 0:
+ tf_dataset = dataset_to_tf(
+ dataset=dataset,
+ cols_to_retain=cols_to_retain,
+ collate_fn=collate_fn,
+ collate_fn_args=collate_fn_args,
+ columns_to_np_types=columns_to_np_types,
+ output_signature=output_signature,
+ shuffle=shuffle,
+ batch_size=batch_size,
+ drop_remainder=drop_remainder,
+ )
+ elif num_workers > 0:
+ if batch_size is None:
+ raise NotImplementedError(
+ "`batch_size` must be specified when using multiple workers, as unbatched multiprocessing "
+ "is not supported yet. Please provide a `batch_size` if `num_workers` is greater than 0."
+ )
+ tf_dataset = multiprocess_dataset_to_tf(
+ dataset=dataset,
+ cols_to_retain=cols_to_retain,
+ collate_fn=collate_fn,
+ collate_fn_args=collate_fn_args,
+ columns_to_np_types=columns_to_np_types,
+ output_signature=output_signature,
+ shuffle=shuffle,
+ batch_size=batch_size,
+ drop_remainder=drop_remainder,
+ num_workers=num_workers,
+ )
+ else:
+ raise ValueError("num_workers must be >= 0")
+
+ def split_features_and_labels(input_batch):
+ # TODO(Matt, QL): deprecate returning the dict content when there's only one key
+ features = {key: tensor for key, tensor in input_batch.items() if key in columns}
+ labels = {key: tensor for key, tensor in input_batch.items() if key in label_cols}
+ if len(features) == 1:
+ features = list(features.values())[0]
+ if len(labels) == 1:
+ labels = list(labels.values())[0]
+ if isinstance(labels, dict) and len(labels) == 0:
+ return features
+ else:
+ return features, labels
+
+ if cols_to_retain is not None:
+ tf_dataset = tf_dataset.map(split_features_and_labels)
+
+ if prefetch:
+ tf_dataset = tf_dataset.prefetch(tf.data.experimental.AUTOTUNE)
+
+ # Remove a reference to the open Arrow file on delete
+ def cleanup_callback(ref):
+ dataset.__del__()
+ self._TF_DATASET_REFS.remove(ref)
+
+ self._TF_DATASET_REFS.add(weakref.ref(tf_dataset, cleanup_callback))
+
+ return tf_dataset
+
+
+class DatasetTransformationNotAllowedError(Exception):
+ pass
+
+
+def transmit_format(func):
+ """Wrapper for dataset transforms that recreate a new Dataset to transmit the format of the original dataset to the new dataset"""
+
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ if args:
+ self: "Dataset" = args[0]
+ args = args[1:]
+ else:
+ self: "Dataset" = kwargs.pop("self")
+ # don't use self.format since it returns a list of columns for 'columns' even if self_format_columns is None
+ unformatted_columns = set(self.column_names) - set(self._format_columns or [])
+ self_format = {
+ "type": self._format_type,
+ "format_kwargs": self._format_kwargs,
+ "columns": self._format_columns,
+ "output_all_columns": self._output_all_columns,
+ }
+ # apply actual function
+ out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
+ datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
+ # re-apply format to the output
+ for dataset in datasets:
+ new_format = self_format.copy()
+ if new_format["columns"] is not None: # new formatted columns = (columns - previously unformatted columns)
+ # sort the columns to have a deterministic list of columns that we can compare with `out_format`
+ new_format["columns"] = sorted(set(dataset.column_names) - unformatted_columns)
+ out_format = {
+ "type": dataset._format_type,
+ "format_kwargs": dataset._format_kwargs,
+ "columns": sorted(dataset._format_columns) if dataset._format_columns is not None else None,
+ "output_all_columns": dataset._output_all_columns,
+ }
+ if out_format != new_format:
+ fingerprint = dataset._fingerprint
+ dataset.set_format(**new_format)
+ dataset._fingerprint = fingerprint
+ return out
+
+ wrapper._decorator_name_ = "transmit_format"
+ return wrapper
+
+
+def transmit_tasks(func):
+ """Wrapper for dataset transforms that recreate a new Dataset to transmit the task templates of the original dataset to the new dataset"""
+
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ if args:
+ self: "Dataset" = args[0]
+ args = args[1:]
+ else:
+ self: "Dataset" = kwargs.pop("self")
+ # apply actual function
+ out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
+ datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
+ for dataset in datasets:
+ # Remove task templates if a column mapping of the template is no longer valid
+ if self.info.task_templates is not None:
+ dataset.info.task_templates = [
+ template
+ for template in self.info.task_templates
+ if all(
+ dataset._info.features.get(k) == self._info.features.get(k)
+ for k in template.column_mapping.keys()
+ )
+ ]
+ return out
+
+ wrapper._decorator_name_ = "transmit_tasks"
+ return wrapper
+
+
+def update_metadata_with_features(table: Table, features: Features):
+ """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema."""
+ features = Features({col_name: features[col_name] for col_name in table.column_names})
+ if table.schema.metadata is None or b"huggingface" not in table.schema.metadata:
+ pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))
+ else:
+ metadata = json.loads(table.schema.metadata[b"huggingface"].decode())
+ if "info" not in metadata:
+ metadata["info"] = asdict(DatasetInfo(features=features))
+ else:
+ metadata["info"]["features"] = asdict(DatasetInfo(features=features))["features"]
+ pa_metadata = {"huggingface": json.dumps(metadata)}
+ table = table.replace_schema_metadata(pa_metadata)
+ return table
+
+
+def _check_table(table) -> Table:
+ """We check the table type to make sure it's an instance of :class:`datasets.table.Table`"""
+ if isinstance(table, pa.Table):
+ # for a pyarrow table, we can just consider it as a in-memory table
+ # this is here for backward compatibility
+ return InMemoryTable(table)
+ elif isinstance(table, Table):
+ return table
+ else:
+ raise TypeError(f"Expected a pyarrow.Table or a datasets.table.Table object, but got {table}.")
+
+
+def _check_column_names(column_names: List[str]):
+ """Check the column names to make sure they don't contain duplicates."""
+ counter = Counter(column_names)
+ if not all(count == 1 for count in counter.values()):
+ duplicated_columns = [col for col in counter if counter[col] > 1]
+ raise ValueError(f"The table can't have duplicated columns but columns {duplicated_columns} are duplicated.")
+
+
+def _check_valid_indices_value(index, size):
+ if (index < 0 and index + size < 0) or (index >= size):
+ raise IndexError(f"Index {index} out of range for dataset of size {size}.")
+
+
+class NonExistentDatasetError(Exception):
+ """Used when we expect the existence of a dataset"""
+
+ pass
+
+
+class Dataset(DatasetInfoMixin, IndexableMixin, TensorflowDatasetMixin):
+ """A Dataset backed by an Arrow table."""
+
+ def __init__(
+ self,
+ arrow_table: Table,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ indices_table: Optional[Table] = None,
+ fingerprint: Optional[str] = None,
+ ):
+ info = info.copy() if info is not None else DatasetInfo()
+ DatasetInfoMixin.__init__(self, info=info, split=split)
+ IndexableMixin.__init__(self)
+
+ self._data: Table = _check_table(arrow_table)
+ self._indices: Optional[Table] = _check_table(indices_table) if indices_table is not None else None
+ maybe_register_dataset_for_temp_dir_deletion(self)
+
+ self._format_type: Optional[str] = None
+ self._format_kwargs: dict = {}
+ self._format_columns: Optional[list] = None
+ self._output_all_columns: bool = False
+ self._fingerprint: str = fingerprint
+
+ # Read metadata
+
+ if self._data.schema.metadata is not None and b"huggingface" in self._data.schema.metadata:
+ metadata = json.loads(self._data.schema.metadata[b"huggingface"].decode())
+ if (
+ "fingerprint" in metadata and self._fingerprint is None
+ ): # try to load fingerprint from the arrow file metadata
+ self._fingerprint = metadata["fingerprint"]
+
+ # Infer features if None
+ inferred_features = Features.from_arrow_schema(arrow_table.schema)
+ if self.info.features is None:
+ self.info.features = inferred_features
+ else: # make sure the nested columns are in the right order
+ try:
+ self.info.features = self.info.features.reorder_fields_as(inferred_features)
+ except ValueError as e:
+ raise ValueError(
+ f"{e}\nThe 'source' features come from dataset_info.json, and the 'target' ones are those of the dataset arrow file."
+ )
+
+ # Infer fingerprint if None
+
+ if self._fingerprint is None:
+ self._fingerprint = generate_fingerprint(self)
+
+ # Sanity checks
+
+ if self._info.features is None:
+ raise ValueError("Features can't be None in a Dataset object")
+ if self._fingerprint is None:
+ raise ValueError("Fingerprint can't be None in a Dataset object")
+ if self.info.features.type != inferred_features.type:
+ raise ValueError(
+ f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}"
+ )
+
+ if self._indices is not None:
+ if not pa.types.is_unsigned_integer(self._indices.column(0).type):
+ raise ValueError(
+ f"indices must be an Arrow table of unsigned integers, current type is {self._indices.column(0).type}"
+ )
+ _check_column_names(self._data.column_names)
+
+ self._data = update_metadata_with_features(self._data, self._info.features)
+
+ @property
+ def features(self) -> Features:
+ features = super().features
+ if features is None: # this is already checked in __init__
+ raise ValueError("Features can't be None in a Dataset object")
+ return features
+
+ @classmethod
+ def from_file(
+ cls,
+ filename: str,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ indices_filename: Optional[str] = None,
+ in_memory: bool = False,
+ ) -> "Dataset":
+ """Instantiate a Dataset backed by an Arrow table at filename.
+
+ Args:
+ filename (`str`):
+ File name of the dataset.
+ info (`DatasetInfo`, *optional*):
+ Dataset information, like description, citation, etc.
+ split (`NamedSplit`, *optional*):
+ Name of the dataset split.
+ indices_filename (`str`, *optional*):
+ File names of the indices.
+ in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+
+ Returns:
+ [`Dataset`]
+ """
+ table = ArrowReader.read_table(filename, in_memory=in_memory)
+
+ if indices_filename is not None:
+ indices_pa_table = ArrowReader.read_table(indices_filename, in_memory=in_memory)
+ else:
+ indices_pa_table = None
+
+ return cls(
+ arrow_table=table,
+ info=info,
+ split=split,
+ indices_table=indices_pa_table,
+ )
+
+ @classmethod
+ def from_buffer(
+ cls,
+ buffer: pa.Buffer,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ indices_buffer: Optional[pa.Buffer] = None,
+ ) -> "Dataset":
+ """Instantiate a Dataset backed by an Arrow buffer.
+
+ Args:
+ buffer (`pyarrow.Buffer`):
+ Arrow buffer.
+ info (`DatasetInfo`, *optional*):
+ Dataset information, like description, citation, etc.
+ split (`NamedSplit`, *optional*):
+ Name of the dataset split.
+ indices_buffer (`pyarrow.Buffer`, *optional*):
+ Indices Arrow buffer.
+
+ Returns:
+ [`Dataset`]
+ """
+ table = InMemoryTable.from_buffer(buffer)
+
+ if indices_buffer is not None:
+ indices_table = InMemoryTable.from_buffer(buffer)
+ else:
+ indices_table = None
+
+ return cls(table, info=info, split=split, indices_table=indices_table)
+
+ @classmethod
+ def from_pandas(
+ cls,
+ df: pd.DataFrame,
+ features: Optional[Features] = None,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ preserve_index: Optional[bool] = None,
+ ) -> "Dataset":
+ """
+ Convert `pandas.DataFrame` to a `pyarrow.Table` to create a [`Dataset`].
+
+ The column types in the resulting Arrow Table are inferred from the dtypes of the `pandas.Series` in the
+ DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the
+ case of `object`, we need to guess the datatype by looking at the Python objects in this Series.
+
+ Be aware that Series of the `object` dtype don't carry enough information to always lead to a meaningful Arrow
+ type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only
+ contains `None/nan` objects, the type is set to `null`. This behavior can be avoided by constructing explicit
+ features and passing it to this function.
+
+ Args:
+ df (`pandas.DataFrame`):
+ Dataframe that contains the dataset.
+ features ([`Features`], *optional*):
+ Dataset features.
+ info (`DatasetInfo`, *optional*):
+ Dataset information, like description, citation, etc.
+ split (`NamedSplit`, *optional*):
+ Name of the dataset split.
+ preserve_index (`bool`, *optional*):
+ Whether to store the index as an additional column in the resulting Dataset.
+ The default of `None` will store the index as a column, except for `RangeIndex` which is stored as metadata only.
+ Use `preserve_index=True` to force it to be stored as a column.
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> ds = Dataset.from_pandas(df)
+ ```
+ """
+ if info is not None and features is not None and info.features != features:
+ raise ValueError(
+ f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
+ )
+ features = features if features is not None else info.features if info is not None else None
+ if info is None:
+ info = DatasetInfo()
+ info.features = features
+ table = InMemoryTable.from_pandas(
+ df=df,
+ preserve_index=preserve_index,
+ )
+ if features is not None:
+ # more expensive cast than InMemoryTable.from_pandas(..., schema=features.arrow_schema)
+ # needed to support the str to Audio conversion for instance
+ table = table.cast(features.arrow_schema)
+ return cls(table, info=info, split=split)
+
+ @classmethod
+ def from_polars(
+ cls,
+ df: "pl.DataFrame",
+ features: Optional[Features] = None,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ ) -> "Dataset":
+ """
+ Collect the underlying arrow arrays in an Arrow Table.
+
+ This operation is mostly zero copy.
+
+ Data types that do copy:
+ * CategoricalType
+
+ Args:
+ df (`polars.DataFrame`): DataFrame to convert to Arrow Table
+ features (`Features`, optional): Dataset features.
+ info (`DatasetInfo`, optional): Dataset information, like description, citation, etc.
+ split (`NamedSplit`, optional): Name of the dataset split.
+
+ Examples:
+ ```py
+ >>> ds = Dataset.from_polars(df)
+ ```
+ """
+ if info is not None and features is not None and info.features != features:
+ raise ValueError(
+ f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
+ )
+ features = features if features is not None else info.features if info is not None else None
+ if info is None:
+ info = DatasetInfo()
+ info.features = features
+ table = InMemoryTable(df.to_arrow())
+ if features is not None:
+ # more expensive cast than InMemoryTable.from_polars(..., schema=features.arrow_schema)
+ # needed to support the str to Audio conversion for instance
+ table = table.cast(features.arrow_schema)
+ return cls(table, info=info, split=split)
+
+ @classmethod
+ def from_dict(
+ cls,
+ mapping: dict,
+ features: Optional[Features] = None,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ ) -> "Dataset":
+ """
+ Convert `dict` to a `pyarrow.Table` to create a [`Dataset`].
+
+ Args:
+ mapping (`Mapping`):
+ Mapping of strings to Arrays or Python lists.
+ features ([`Features`], *optional*):
+ Dataset features.
+ info (`DatasetInfo`, *optional*):
+ Dataset information, like description, citation, etc.
+ split (`NamedSplit`, *optional*):
+ Name of the dataset split.
+
+ Returns:
+ [`Dataset`]
+ """
+ if info is not None and features is not None and info.features != features:
+ raise ValueError(
+ f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
+ )
+ features = features if features is not None else info.features if info is not None else None
+ arrow_typed_mapping = {}
+ for col, data in mapping.items():
+ if isinstance(data, (pa.Array, pa.ChunkedArray)):
+ data = cast_array_to_feature(data, features[col]) if features is not None else data
+ else:
+ data = OptimizedTypedSequence(
+ features.encode_column(data, col) if features is not None else data,
+ type=features[col] if features is not None else None,
+ col=col,
+ )
+ arrow_typed_mapping[col] = data
+ mapping = arrow_typed_mapping
+ pa_table = InMemoryTable.from_pydict(mapping=mapping)
+ if info is None:
+ info = DatasetInfo()
+ info.features = features
+ if info.features is None:
+ info.features = Features(
+ {
+ col: generate_from_arrow_type(data.type)
+ if isinstance(data, (pa.Array, pa.ChunkedArray))
+ else data.get_inferred_type()
+ for col, data in mapping.items()
+ }
+ )
+ return cls(pa_table, info=info, split=split)
+
+ @classmethod
+ def from_list(
+ cls,
+ mapping: List[dict],
+ features: Optional[Features] = None,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ ) -> "Dataset":
+ """
+ Convert a list of dicts to a `pyarrow.Table` to create a [`Dataset`]`.
+
+ Note that the keys of the first entry will be used to determine the dataset columns,
+ regardless of what is passed to features.
+
+ Args:
+ mapping (`List[dict]`): A list of mappings of strings to row values.
+ features (`Features`, optional): Dataset features.
+ info (`DatasetInfo`, optional): Dataset information, like description, citation, etc.
+ split (`NamedSplit`, optional): Name of the dataset split.
+
+ Returns:
+ [`Dataset`]
+ """
+ # for simplicity and consistency wrt OptimizedTypedSequence we do not use InMemoryTable.from_pylist here
+ mapping = {k: [r.get(k) for r in mapping] for k in mapping[0]} if mapping else {}
+ return cls.from_dict(mapping, features, info, split)
+
+ @staticmethod
+ def from_csv(
+ path_or_paths: Union[PathLike, List[PathLike]],
+ split: Optional[NamedSplit] = None,
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Create Dataset from CSV file(s).
+
+ Args:
+ path_or_paths (`path-like` or list of `path-like`):
+ Path(s) of the CSV file(s).
+ split ([`NamedSplit`], *optional*):
+ Split name to be assigned to the dataset.
+ features ([`Features`], *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ This is helpful if the dataset is made of multiple files. Multiprocessing is disabled by default.
+
+
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to [`pandas.read_csv`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> ds = Dataset.from_csv('path/to/dataset.csv')
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.csv import CsvDatasetReader
+
+ return CsvDatasetReader(
+ path_or_paths,
+ split=split,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ num_proc=num_proc,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_generator(
+ generator: Callable,
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ gen_kwargs: Optional[dict] = None,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Create a Dataset from a generator.
+
+ Args:
+ generator (:`Callable`):
+ A generator function that `yields` examples.
+ features ([`Features`], *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ gen_kwargs(`dict`, *optional*):
+ Keyword arguments to be passed to the `generator` callable.
+ You can define a sharded dataset by passing the list of shards in `gen_kwargs` and setting `num_proc` greater than 1.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ This is helpful if the dataset is made of multiple files. Multiprocessing is disabled by default.
+ If `num_proc` is greater than one, then all list values in `gen_kwargs` must be the same length. These values will be split between calls to the generator. The number of shards will be the minimum of the shortest list in `gen_kwargs` and `num_proc`.
+
+
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to :[`GeneratorConfig`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> def gen():
+ ... yield {"text": "Good", "label": 0}
+ ... yield {"text": "Bad", "label": 1}
+ ...
+ >>> ds = Dataset.from_generator(gen)
+ ```
+
+ ```py
+ >>> def gen(shards):
+ ... for shard in shards:
+ ... with open(shard) as f:
+ ... for line in f:
+ ... yield {"line": line}
+ ...
+ >>> shards = [f"data{i}.txt" for i in range(32)]
+ >>> ds = Dataset.from_generator(gen, gen_kwargs={"shards": shards})
+ ```
+ """
+ from .io.generator import GeneratorDatasetInputStream
+
+ return GeneratorDatasetInputStream(
+ generator=generator,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ gen_kwargs=gen_kwargs,
+ num_proc=num_proc,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_json(
+ path_or_paths: Union[PathLike, List[PathLike]],
+ split: Optional[NamedSplit] = None,
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ field: Optional[str] = None,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Create Dataset from JSON or JSON Lines file(s).
+
+ Args:
+ path_or_paths (`path-like` or list of `path-like`):
+ Path(s) of the JSON or JSON Lines file(s).
+ split ([`NamedSplit`], *optional*):
+ Split name to be assigned to the dataset.
+ features ([`Features`], *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ field (`str`, *optional*):
+ Field name of the JSON file where the dataset is contained in.
+ num_proc (`int`, *optional* defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ This is helpful if the dataset is made of multiple files. Multiprocessing is disabled by default.
+
+
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to [`JsonConfig`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> ds = Dataset.from_json('path/to/dataset.json')
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.json import JsonDatasetReader
+
+ return JsonDatasetReader(
+ path_or_paths,
+ split=split,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ field=field,
+ num_proc=num_proc,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_parquet(
+ path_or_paths: Union[PathLike, List[PathLike]],
+ split: Optional[NamedSplit] = None,
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ columns: Optional[List[str]] = None,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Create Dataset from Parquet file(s).
+
+ Args:
+ path_or_paths (`path-like` or list of `path-like`):
+ Path(s) of the Parquet file(s).
+ split (`NamedSplit`, *optional*):
+ Split name to be assigned to the dataset.
+ features (`Features`, *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ columns (`List[str]`, *optional*):
+ If not `None`, only these columns will be read from the file.
+ A column name may be a prefix of a nested field, e.g. 'a' will select
+ 'a.b', 'a.c', and 'a.d.e'.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ This is helpful if the dataset is made of multiple files. Multiprocessing is disabled by default.
+
+
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to [`ParquetConfig`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> ds = Dataset.from_parquet('path/to/dataset.parquet')
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.parquet import ParquetDatasetReader
+
+ return ParquetDatasetReader(
+ path_or_paths,
+ split=split,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ columns=columns,
+ num_proc=num_proc,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_text(
+ path_or_paths: Union[PathLike, List[PathLike]],
+ split: Optional[NamedSplit] = None,
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ num_proc: Optional[int] = None,
+ **kwargs,
+ ):
+ """Create Dataset from text file(s).
+
+ Args:
+ path_or_paths (`path-like` or list of `path-like`):
+ Path(s) of the text file(s).
+ split (`NamedSplit`, *optional*):
+ Split name to be assigned to the dataset.
+ features (`Features`, *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes when downloading and generating the dataset locally.
+ This is helpful if the dataset is made of multiple files. Multiprocessing is disabled by default.
+
+
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to [`TextConfig`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> ds = Dataset.from_text('path/to/dataset.txt')
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.text import TextDatasetReader
+
+ return TextDatasetReader(
+ path_or_paths,
+ split=split,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ num_proc=num_proc,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_spark(
+ df: "pyspark.sql.DataFrame",
+ split: Optional[NamedSplit] = None,
+ features: Optional[Features] = None,
+ keep_in_memory: bool = False,
+ cache_dir: str = None,
+ working_dir: str = None,
+ load_from_cache_file: bool = True,
+ **kwargs,
+ ):
+ """Create a Dataset from Spark DataFrame. Dataset downloading is distributed over Spark workers.
+
+ Args:
+ df (`pyspark.sql.DataFrame`):
+ The DataFrame containing the desired data.
+ split (`NamedSplit`, *optional*):
+ Split name to be assigned to the dataset.
+ features (`Features`, *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data. When using a multi-node Spark cluster, the cache_dir must be accessible to both
+ workers and the driver.
+ keep_in_memory (`bool`):
+ Whether to copy the data in-memory.
+ working_dir (`str`, *optional*)
+ Intermediate directory for each Spark worker to write data to before moving it to `cache_dir`. Setting
+ a non-NFS intermediate directory may improve performance.
+ load_from_cache_file (`bool`):
+ Whether to load the dataset from the cache if possible.
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> df = spark.createDataFrame(
+ >>> data=[[1, "Elia"], [2, "Teo"], [3, "Fang"]],
+ >>> columns=["id", "name"],
+ >>> )
+ >>> ds = Dataset.from_spark(df)
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.spark import SparkDatasetReader
+
+ if sys.platform == "win32":
+ raise EnvironmentError("Dataset.from_spark is not currently supported on Windows")
+
+ return SparkDatasetReader(
+ df,
+ split=split,
+ features=features,
+ streaming=False,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ working_dir=working_dir,
+ load_from_cache_file=load_from_cache_file,
+ **kwargs,
+ ).read()
+
+ @staticmethod
+ def from_sql(
+ sql: Union[str, "sqlalchemy.sql.Selectable"],
+ con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
+ features: Optional[Features] = None,
+ cache_dir: str = None,
+ keep_in_memory: bool = False,
+ **kwargs,
+ ):
+ """Create Dataset from SQL query or database table.
+
+ Args:
+ sql (`str` or `sqlalchemy.sql.Selectable`):
+ SQL query to be executed or a table name.
+ con (`str` or `sqlite3.Connection` or `sqlalchemy.engine.Connection` or `sqlalchemy.engine.Connection`):
+ A [URI string](https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls) used to instantiate a database connection or a SQLite3/SQLAlchemy connection object.
+ features ([`Features`], *optional*):
+ Dataset features.
+ cache_dir (`str`, *optional*, defaults to `"~/.cache/huggingface/datasets"`):
+ Directory to cache data.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ **kwargs (additional keyword arguments):
+ Keyword arguments to be passed to [`SqlConfig`].
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> # Fetch a database table
+ >>> ds = Dataset.from_sql("test_data", "postgres:///db_name")
+ >>> # Execute a SQL query on the table
+ >>> ds = Dataset.from_sql("SELECT sentence FROM test_data", "postgres:///db_name")
+ >>> # Use a Selectable object to specify the query
+ >>> from sqlalchemy import select, text
+ >>> stmt = select([text("sentence")]).select_from(text("test_data"))
+ >>> ds = Dataset.from_sql(stmt, "postgres:///db_name")
+ ```
+
+
+
+ The returned dataset can only be cached if `con` is specified as URI string.
+
+
+ """
+ from .io.sql import SqlDatasetReader
+
+ return SqlDatasetReader(
+ sql,
+ con,
+ features=features,
+ cache_dir=cache_dir,
+ keep_in_memory=keep_in_memory,
+ **kwargs,
+ ).read()
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+ maybe_register_dataset_for_temp_dir_deletion(self)
+ return self
+
+ def __del__(self):
+ if hasattr(self, "_data"):
+ del self._data
+ if hasattr(self, "_indices"):
+ del self._indices
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ # Here `del` is used to del the pyarrow tables. This properly closes the files used for memory mapped tables
+ self.__del__()
+
+ def save_to_disk(
+ self,
+ dataset_path: PathLike,
+ fs="deprecated",
+ max_shard_size: Optional[Union[str, int]] = None,
+ num_shards: Optional[int] = None,
+ num_proc: Optional[int] = None,
+ storage_options: Optional[dict] = None,
+ ):
+ """
+ Saves a dataset to a dataset directory, or in a filesystem using any implementation of `fsspec.spec.AbstractFileSystem`.
+
+ For [`Image`] and [`Audio`] data:
+
+ All the Image() and Audio() data are stored in the arrow files.
+ If you want to store paths or urls, please use the Value("string") type.
+
+ Args:
+ dataset_path (`str`):
+ Path (e.g. `dataset/train`) or remote URI (e.g. `s3://my-bucket/dataset/train`)
+ of the dataset directory where the dataset will be saved to.
+ fs (`fsspec.spec.AbstractFileSystem`, *optional*):
+ Instance of the remote filesystem where the dataset will be saved to.
+
+
+
+ `fs` was deprecated in version 2.8.0 and will be removed in 3.0.0.
+ Please use `storage_options` instead, e.g. `storage_options=fs.storage_options`
+
+
+
+ max_shard_size (`int` or `str`, *optional*, defaults to `"500MB"`):
+ The maximum size of the dataset shards to be uploaded to the hub. If expressed as a string, needs to be digits followed by a unit
+ (like `"50MB"`).
+ num_shards (`int`, *optional*):
+ Number of shards to write. By default the number of shards depends on `max_shard_size` and `num_proc`.
+
+
+ num_proc (`int`, *optional*):
+ Number of processes when downloading and generating the dataset locally.
+ Multiprocessing is disabled by default.
+
+
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the file-system backend, if any.
+
+
+
+ Example:
+
+ ```py
+ >>> ds.save_to_disk("path/to/dataset/directory")
+ >>> ds.save_to_disk("path/to/dataset/directory", max_shard_size="1GB")
+ >>> ds.save_to_disk("path/to/dataset/directory", num_shards=1024)
+ ```
+ """
+ if max_shard_size is not None and num_shards is not None:
+ raise ValueError(
+ "Failed to push_to_hub: please specify either max_shard_size or num_shards, but not both."
+ )
+ if fs != "deprecated":
+ warnings.warn(
+ "'fs' was deprecated in favor of 'storage_options' in version 2.8.0 and will be removed in 3.0.0.\n"
+ "You can remove this warning by passing 'storage_options=fs.storage_options' instead.",
+ FutureWarning,
+ )
+ storage_options = fs.storage_options
+
+ if self.list_indexes():
+ raise ValueError("please remove all the indexes using `dataset.drop_index` before saving a dataset")
+
+ if num_shards is None:
+ dataset_nbytes = self._estimate_nbytes()
+ max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
+ num_shards = int(dataset_nbytes / max_shard_size) + 1
+ num_shards = max(num_shards, num_proc or 1)
+
+ num_proc = num_proc if num_proc is not None else 1
+ num_shards = num_shards if num_shards is not None else num_proc
+
+ fs: fsspec.AbstractFileSystem
+ fs, _ = url_to_fs(dataset_path, **(storage_options or {}))
+
+ if not is_remote_filesystem(fs):
+ parent_cache_files_paths = {
+ Path(cache_filename["filename"]).resolve().parent for cache_filename in self.cache_files
+ }
+ # Check that the dataset doesn't overwrite iself. It can cause a permission error on Windows and a segfault on linux.
+ if Path(dataset_path).expanduser().resolve() in parent_cache_files_paths:
+ raise PermissionError(
+ f"Tried to overwrite {Path(dataset_path).expanduser().resolve()} but a dataset can't overwrite itself."
+ )
+
+ fs.makedirs(dataset_path, exist_ok=True)
+
+ # Get json serializable state
+ state = {
+ key: self.__dict__[key]
+ for key in [
+ "_fingerprint",
+ "_format_columns",
+ "_format_kwargs",
+ "_format_type",
+ "_output_all_columns",
+ ]
+ }
+ state["_split"] = str(self.split) if self.split is not None else self.split
+ state["_data_files"] = [
+ {"filename": f"data-{shard_idx:05d}-of-{num_shards:05d}.arrow"} for shard_idx in range(num_shards)
+ ]
+ for k in state["_format_kwargs"].keys():
+ try:
+ json.dumps(state["_format_kwargs"][k])
+ except TypeError as e:
+ raise TypeError(
+ str(e) + f"\nThe format kwargs must be JSON serializable, but key '{k}' isn't."
+ ) from None
+ # Get json serializable dataset info
+ dataset_info = asdict(self._info)
+
+ shards_done = 0
+ pbar = hf_tqdm(
+ unit=" examples",
+ total=len(self),
+ desc=f"Saving the dataset ({shards_done}/{num_shards} shards)",
+ )
+ kwargs_per_job = (
+ {
+ "job_id": shard_idx,
+ "shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True),
+ "fpath": posixpath.join(dataset_path, f"data-{shard_idx:05d}-of-{num_shards:05d}.arrow"),
+ "storage_options": storage_options,
+ }
+ for shard_idx in range(num_shards)
+ )
+ shard_lengths = [None] * num_shards
+ shard_sizes = [None] * num_shards
+ if num_proc > 1:
+ with Pool(num_proc) as pool:
+ with pbar:
+ for job_id, done, content in iflatmap_unordered(
+ pool, Dataset._save_to_disk_single, kwargs_iterable=kwargs_per_job
+ ):
+ if done:
+ shards_done += 1
+ pbar.set_description(f"Saving the dataset ({shards_done}/{num_shards} shards)")
+ logger.debug(f"Finished writing shard number {job_id} of {num_shards}.")
+ shard_lengths[job_id], shard_sizes[job_id] = content
+ else:
+ pbar.update(content)
+ else:
+ with pbar:
+ for kwargs in kwargs_per_job:
+ for job_id, done, content in Dataset._save_to_disk_single(**kwargs):
+ if done:
+ shards_done += 1
+ pbar.set_description(f"Saving the dataset ({shards_done}/{num_shards} shards)")
+ logger.debug(f"Finished writing shard number {job_id} of {num_shards}.")
+ shard_lengths[job_id], shard_sizes[job_id] = content
+ else:
+ pbar.update(content)
+ with fs.open(
+ posixpath.join(dataset_path, config.DATASET_STATE_JSON_FILENAME), "w", encoding="utf-8"
+ ) as state_file:
+ json.dump(state, state_file, indent=2, sort_keys=True)
+ with fs.open(
+ posixpath.join(dataset_path, config.DATASET_INFO_FILENAME), "w", encoding="utf-8"
+ ) as dataset_info_file:
+ # Sort only the first level of keys, or we might shuffle fields of nested features if we use sort_keys=True
+ sorted_keys_dataset_info = {key: dataset_info[key] for key in sorted(dataset_info)}
+ json.dump(sorted_keys_dataset_info, dataset_info_file, indent=2)
+
+ @staticmethod
+ def _save_to_disk_single(job_id: int, shard: "Dataset", fpath: str, storage_options: Optional[dict]):
+ batch_size = config.DEFAULT_MAX_BATCH_SIZE
+
+ num_examples_progress_update = 0
+ writer = ArrowWriter(
+ features=shard.features,
+ path=fpath,
+ storage_options=storage_options,
+ embed_local_files=True,
+ )
+ try:
+ _time = time.time()
+ for pa_table in shard.with_format("arrow").iter(batch_size):
+ writer.write_table(pa_table)
+ num_examples_progress_update += len(pa_table)
+ if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
+ _time = time.time()
+ yield job_id, False, num_examples_progress_update
+ num_examples_progress_update = 0
+ finally:
+ yield job_id, False, num_examples_progress_update
+ num_examples, num_bytes = writer.finalize()
+ writer.close()
+
+ yield job_id, True, (num_examples, num_bytes)
+
+ @staticmethod
+ def _build_local_temp_path(uri_or_path: str) -> Path:
+ """
+ Builds and returns a Path concatenating a local temporary dir with the dir path (or absolute/relative
+ path extracted from the uri) passed.
+
+ Args:
+ uri_or_path (`str`): Path (e.g. `"dataset/train"`) or remote URI (e.g.
+ `"s3://my-bucket/dataset/train"`) to concatenate.
+
+ Returns:
+ :class:`Path`: the concatenated path (temp dir + path)
+ """
+ src_dataset_path = Path(uri_or_path)
+ tmp_dir = get_temporary_cache_files_directory()
+ return Path(tmp_dir, src_dataset_path.relative_to(src_dataset_path.anchor))
+
+ @staticmethod
+ def load_from_disk(
+ dataset_path: str,
+ fs="deprecated",
+ keep_in_memory: Optional[bool] = None,
+ storage_options: Optional[dict] = None,
+ ) -> "Dataset":
+ """
+ Loads a dataset that was previously saved using [`save_to_disk`] from a dataset directory, or from a
+ filesystem using any implementation of `fsspec.spec.AbstractFileSystem`.
+
+ Args:
+ dataset_path (`str`):
+ Path (e.g. `"dataset/train"`) or remote URI (e.g. `"s3//my-bucket/dataset/train"`)
+ of the dataset directory where the dataset will be loaded from.
+ fs (`fsspec.spec.AbstractFileSystem`, *optional*):
+ Instance of the remote filesystem where the dataset will be saved to.
+
+
+
+ `fs` was deprecated in version 2.8.0 and will be removed in 3.0.0.
+ Please use `storage_options` instead, e.g. `storage_options=fs.storage_options`
+
+
+
+ keep_in_memory (`bool`, defaults to `None`):
+ Whether to copy the dataset in-memory. If `None`, the
+ dataset will not be copied in-memory unless explicitly enabled by setting
+ `datasets.config.IN_MEMORY_MAX_SIZE` to nonzero. See more details in the
+ [improve performance](../cache#improve-performance) section.
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the file-system backend, if any.
+
+
+
+ Returns:
+ [`Dataset`] or [`DatasetDict`]:
+ - If `dataset_path` is a path of a dataset directory, the dataset requested.
+ - If `dataset_path` is a path of a dataset dict directory, a `datasets.DatasetDict` with each split.
+
+ Example:
+
+ ```py
+ >>> ds = load_from_disk("path/to/dataset/directory")
+ ```
+ """
+ if fs != "deprecated":
+ warnings.warn(
+ "'fs' was deprecated in favor of 'storage_options' in version 2.8.0 and will be removed in 3.0.0.\n"
+ "You can remove this warning by passing 'storage_options=fs.storage_options' instead.",
+ FutureWarning,
+ )
+ storage_options = fs.storage_options
+
+ fs: fsspec.AbstractFileSystem
+ fs, dataset_path = url_to_fs(dataset_path, **(storage_options or {}))
+
+ dest_dataset_path = dataset_path
+ dataset_dict_json_path = posixpath.join(dest_dataset_path, config.DATASETDICT_JSON_FILENAME)
+ dataset_state_json_path = posixpath.join(dest_dataset_path, config.DATASET_STATE_JSON_FILENAME)
+ dataset_info_path = posixpath.join(dest_dataset_path, config.DATASET_INFO_FILENAME)
+
+ dataset_dict_is_file = fs.isfile(dataset_dict_json_path)
+ dataset_info_is_file = fs.isfile(dataset_info_path)
+ dataset_state_is_file = fs.isfile(dataset_state_json_path)
+ if not dataset_info_is_file and not dataset_state_is_file:
+ if dataset_dict_is_file:
+ raise FileNotFoundError(
+ f"No such files: '{dataset_info_path}', nor '{dataset_state_json_path}' found. Expected to load a `Dataset` object, but got a `DatasetDict`. Please use either `datasets.load_from_disk` or `DatasetDict.load_from_disk` instead."
+ )
+ raise FileNotFoundError(
+ f"No such files: '{dataset_info_path}', nor '{dataset_state_json_path}' found. Expected to load a `Dataset` object but provided path is not a `Dataset`."
+ )
+ if not dataset_info_is_file:
+ if dataset_dict_is_file:
+ raise FileNotFoundError(
+ f"No such file: '{dataset_info_path}' found. Expected to load a `Dataset` object, but got a `DatasetDict`. Please use either `datasets.load_from_disk` or `DatasetDict.load_from_disk` instead."
+ )
+ raise FileNotFoundError(
+ f"No such file: '{dataset_info_path}'. Expected to load a `Dataset` object but provided path is not a `Dataset`."
+ )
+ if not dataset_state_is_file:
+ if dataset_dict_is_file:
+ raise FileNotFoundError(
+ f"No such file: '{dataset_state_json_path}' found. Expected to load a `Dataset` object, but got a `DatasetDict`. Please use either `datasets.load_from_disk` or `DatasetDict.load_from_disk` instead."
+ )
+ raise FileNotFoundError(
+ f"No such file: '{dataset_state_json_path}'. Expected to load a `Dataset` object but provided path is not a `Dataset`."
+ )
+
+ # copies file from filesystem if it is remote filesystem to local filesystem and modifies dataset_path to temp directory containing local copies
+ if is_remote_filesystem(fs):
+ src_dataset_path = dest_dataset_path
+ dest_dataset_path = Dataset._build_local_temp_path(src_dataset_path)
+ fs.download(src_dataset_path, dest_dataset_path.as_posix(), recursive=True)
+ dataset_state_json_path = posixpath.join(dest_dataset_path, config.DATASET_STATE_JSON_FILENAME)
+ dataset_info_path = posixpath.join(dest_dataset_path, config.DATASET_INFO_FILENAME)
+
+ with open(dataset_state_json_path, encoding="utf-8") as state_file:
+ state = json.load(state_file)
+ with open(dataset_info_path, encoding="utf-8") as dataset_info_file:
+ dataset_info = DatasetInfo.from_dict(json.load(dataset_info_file))
+
+ dataset_size = estimate_dataset_size(
+ Path(dest_dataset_path, data_file["filename"]) for data_file in state["_data_files"]
+ )
+ keep_in_memory = keep_in_memory if keep_in_memory is not None else is_small_dataset(dataset_size)
+ table_cls = InMemoryTable if keep_in_memory else MemoryMappedTable
+
+ arrow_table = concat_tables(
+ thread_map(
+ table_cls.from_file,
+ [posixpath.join(dest_dataset_path, data_file["filename"]) for data_file in state["_data_files"]],
+ tqdm_class=hf_tqdm,
+ desc="Loading dataset from disk",
+ # set `disable=None` rather than `disable=False` by default to disable progress bar when no TTY attached
+ disable=len(state["_data_files"]) <= 16 or None,
+ )
+ )
+
+ split = state["_split"]
+ split = Split(split) if split is not None else split
+
+ dataset = Dataset(
+ arrow_table=arrow_table,
+ info=dataset_info,
+ split=split,
+ fingerprint=state["_fingerprint"],
+ )
+
+ format = {
+ "type": state["_format_type"],
+ "format_kwargs": state["_format_kwargs"],
+ "columns": state["_format_columns"],
+ "output_all_columns": state["_output_all_columns"],
+ }
+ dataset = dataset.with_format(**format)
+
+ return dataset
+
+ @property
+ def data(self) -> Table:
+ """The Apache Arrow table backing the dataset.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.data
+ MemoryMappedTable
+ text: string
+ label: int64
+ ----
+ text: [["compassionately explores the seemingly irreconcilable situation between conservative christian parents and their estranged gay and lesbian children .","the soundtrack alone is worth the price of admission .","rodriguez does a splendid job of racial profiling hollywood style--casting excellent latin actors of all ages--a trend long overdue .","beneath the film's obvious determination to shock at any cost lies considerable skill and determination , backed by sheer nerve .","bielinsky is a filmmaker of impressive talent .","so beautifully acted and directed , it's clear that washington most certainly has a new career ahead of him if he so chooses .","a visual spectacle full of stunning images and effects .","a gentle and engrossing character study .","it's enough to watch huppert scheming , with her small , intelligent eyes as steady as any noir villain , and to enjoy the perfectly pitched web of tension that chabrol spins .","an engrossing portrait of uncompromising artists trying to create something original against the backdrop of a corporate music industry that only seems to care about the bottom line .",...,"ultimately , jane learns her place as a girl , softens up and loses some of the intensity that made her an interesting character to begin with .","ah-nuld's action hero days might be over .","it's clear why deuces wild , which was shot two years ago , has been gathering dust on mgm's shelf .","feels like nothing quite so much as a middle-aged moviemaker's attempt to surround himself with beautiful , half-naked women .","when the precise nature of matthew's predicament finally comes into sharp focus , the revelation fails to justify the build-up .","this picture is murder by numbers , and as easy to be bored by as your abc's , despite a few whopping shootouts .","hilarious musical comedy though stymied by accents thick as mud .","if you are into splatter movies , then you will probably have a reasonably good time with the salton sea .","a dull , simple-minded and stereotypical tale of drugs , death and mind-numbing indifference on the inner-city streets .","the feature-length stretch . . . strains the show's concept ."]]
+ label: [[1,1,1,1,1,1,1,1,1,1,...,0,0,0,0,0,0,0,0,0,0]]
+ ```
+ """
+ return self._data
+
+ @property
+ def cache_files(self) -> List[dict]:
+ """The cache files containing the Apache Arrow table backing the dataset.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.cache_files
+ [{'filename': '/root/.cache/huggingface/datasets/rotten_tomatoes_movie_review/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46/rotten_tomatoes_movie_review-validation.arrow'}]
+ ```
+ """
+ cache_files = list_table_cache_files(self._data)
+ if self._indices is not None:
+ cache_files += list_table_cache_files(self._indices)
+ return [{"filename": cache_filename} for cache_filename in cache_files]
+
+ @property
+ def num_columns(self) -> int:
+ """Number of columns in the dataset.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.num_columns
+ 2
+ ```
+ """
+ return self._data.num_columns
+
+ @property
+ def num_rows(self) -> int:
+ """Number of rows in the dataset (same as [`Dataset.__len__`]).
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.num_rows
+ 1066
+ ```
+ """
+ if self._indices is not None:
+ return self._indices.num_rows
+ return self._data.num_rows
+
+ @property
+ def column_names(self) -> List[str]:
+ """Names of the columns in the dataset.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.column_names
+ ['text', 'label']
+ ```
+ """
+ return self._data.column_names
+
+ @property
+ def shape(self) -> Tuple[int, int]:
+ """Shape of the dataset (number of columns, number of rows).
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.shape
+ (1066, 2)
+ ```
+ """
+ if self._indices is not None:
+ return (self._indices.num_rows, self._data.num_columns)
+ return self._data.shape
+
+ def unique(self, column: str) -> List:
+ """Return a list of the unique elements in a column.
+
+ This is implemented in the low-level backend and as such, very fast.
+
+ Args:
+ column (`str`):
+ Column name (list all the column names with [`~datasets.Dataset.column_names`]).
+
+ Returns:
+ `list`: List of unique elements in the given column.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.unique('label')
+ [1, 0]
+ ```
+ """
+ if column not in self._data.column_names:
+ raise ValueError(f"Column ({column}) not in table columns ({self._data.column_names}).")
+
+ if self._indices is not None and self._indices.num_rows != self._data.num_rows:
+ dataset = self.flatten_indices()
+ else:
+ dataset = self
+
+ return dataset._data.column(column).unique().to_pylist()
+
+ def class_encode_column(self, column: str, include_nulls: bool = False) -> "Dataset":
+ """Casts the given column as [`~datasets.features.ClassLabel`] and updates the table.
+
+ Args:
+ column (`str`):
+ The name of the column to cast (list all the column names with [`~datasets.Dataset.column_names`])
+ include_nulls (`bool`, defaults to `False`):
+ Whether to include null values in the class labels. If `True`, the null values will be encoded as the `"None"` class label.
+
+
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("boolq", split="validation")
+ >>> ds.features
+ {'answer': Value(dtype='bool', id=None),
+ 'passage': Value(dtype='string', id=None),
+ 'question': Value(dtype='string', id=None)}
+ >>> ds = ds.class_encode_column('answer')
+ >>> ds.features
+ {'answer': ClassLabel(num_classes=2, names=['False', 'True'], id=None),
+ 'passage': Value(dtype='string', id=None),
+ 'question': Value(dtype='string', id=None)}
+ ```
+ """
+ # Sanity checks
+ if column not in self._data.column_names:
+ raise ValueError(f"Column ({column}) not in table columns ({self._data.column_names}).")
+ src_feat = self._info.features[column]
+ if not isinstance(src_feat, Value):
+ raise ValueError(
+ f"Class encoding is only supported for {Value.__name__} column, and column {column} is {type(src_feat).__name__}."
+ )
+
+ if src_feat.dtype != "string" or (include_nulls and None in self.unique(column)):
+
+ def stringify_column(batch):
+ batch[column] = [
+ str(sample) if include_nulls or sample is not None else None for sample in batch[column]
+ ]
+ return batch
+
+ dset = self.map(
+ stringify_column,
+ batched=True,
+ desc="Stringifying the column",
+ )
+ else:
+ dset = self
+
+ # Create the new feature
+ class_names = sorted(str(sample) for sample in dset.unique(column) if include_nulls or sample is not None)
+ dst_feat = ClassLabel(names=class_names)
+
+ def cast_to_class_labels(batch):
+ batch[column] = [
+ dst_feat.str2int(str(sample)) if include_nulls or sample is not None else None
+ for sample in batch[column]
+ ]
+ return batch
+
+ new_features = dset.features.copy()
+ new_features[column] = dst_feat
+
+ dset = dset.map(
+ cast_to_class_labels,
+ batched=True,
+ features=new_features,
+ desc="Casting to class labels",
+ )
+
+ return dset
+
+ @fingerprint_transform(inplace=False)
+ def flatten(self, new_fingerprint: Optional[str] = None, max_depth=16) -> "Dataset":
+ """Flatten the table.
+ Each column with a struct type is flattened into one column per struct field.
+ Other columns are left unchanged.
+
+ Args:
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset with flattened columns.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("squad", split="train")
+ >>> ds.features
+ {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None),
+ 'context': Value(dtype='string', id=None),
+ 'id': Value(dtype='string', id=None),
+ 'question': Value(dtype='string', id=None),
+ 'title': Value(dtype='string', id=None)}
+ >>> ds.flatten()
+ Dataset({
+ features: ['id', 'title', 'context', 'question', 'answers.text', 'answers.answer_start'],
+ num_rows: 87599
+ })
+ ```
+ """
+ dataset = copy.deepcopy(self)
+ for depth in range(1, max_depth):
+ if any(isinstance(field.type, pa.StructType) for field in dataset._data.schema):
+ dataset._data = dataset._data.flatten()
+ else:
+ break
+ dataset.info.features = self._info.features.flatten(max_depth=max_depth)
+ dataset.info.features = Features({col: dataset.info.features[col] for col in dataset.data.column_names})
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.')
+ dataset._fingerprint = new_fingerprint
+ return dataset
+
+ def cast(
+ self,
+ features: Features,
+ batch_size: Optional[int] = 1000,
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ num_proc: Optional[int] = None,
+ ) -> "Dataset":
+ """
+ Cast the dataset to a new set of features.
+
+ Args:
+ features ([`Features`]):
+ New features to cast the dataset to.
+ The name of the fields in the features must match the current column names.
+ The type of the data must also be convertible from one type to the other.
+ For non-trivial conversion, e.g. `str` <-> `ClassLabel` you should use [`~datasets.Dataset.map`] to update the Dataset.
+ batch_size (`int`, defaults to `1000`):
+ Number of examples per batch provided to cast.
+ If `batch_size <= 0` or `batch_size == None` then provide the full dataset as a single batch to cast.
+ keep_in_memory (`bool`, defaults to `False`):
+ Whether to copy the data in-memory.
+ load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
+ If a cache file storing the current computation from `function`
+ can be identified, use it instead of recomputing.
+ cache_file_name (`str`, *optional*, defaults to `None`):
+ Provide the name of a path for the cache file. It is used to store the
+ results of the computation instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running [`~datasets.Dataset.map`].
+ num_proc (`int`, *optional*, defaults to `None`):
+ Number of processes for multiprocessing. By default it doesn't
+ use multiprocessing.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset with casted features.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset, ClassLabel, Value
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.features
+ {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None),
+ 'text': Value(dtype='string', id=None)}
+ >>> new_features = ds.features.copy()
+ >>> new_features['label'] = ClassLabel(names=['bad', 'good'])
+ >>> new_features['text'] = Value('large_string')
+ >>> ds = ds.cast(new_features)
+ >>> ds.features
+ {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None),
+ 'text': Value(dtype='large_string', id=None)}
+ ```
+ """
+ if sorted(features) != sorted(self._data.column_names):
+ raise ValueError(
+ f"The columns in features ({list(features)}) must be identical "
+ f"as the columns in the dataset: {self._data.column_names}"
+ )
+
+ schema = features.arrow_schema
+ format = self.format
+ dataset = self.with_format("arrow")
+ # capture the PyArrow version here to make the lambda serializable on Windows
+ dataset = dataset.map(
+ partial(table_cast, schema=schema),
+ batched=True,
+ batch_size=batch_size,
+ keep_in_memory=keep_in_memory,
+ load_from_cache_file=load_from_cache_file,
+ cache_file_name=cache_file_name,
+ writer_batch_size=writer_batch_size,
+ num_proc=num_proc,
+ features=features,
+ desc="Casting the dataset",
+ )
+ dataset = dataset.with_format(**format)
+ return dataset
+
+ @fingerprint_transform(inplace=False)
+ def cast_column(self, column: str, feature: FeatureType, new_fingerprint: Optional[str] = None) -> "Dataset":
+ """Cast column to feature for decoding.
+
+ Args:
+ column (`str`):
+ Column name.
+ feature (`FeatureType`):
+ Target feature.
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.features
+ {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None),
+ 'text': Value(dtype='string', id=None)}
+ >>> ds = ds.cast_column('label', ClassLabel(names=['bad', 'good']))
+ >>> ds.features
+ {'label': ClassLabel(num_classes=2, names=['bad', 'good'], id=None),
+ 'text': Value(dtype='string', id=None)}
+ ```
+ """
+ if hasattr(feature, "decode_example"):
+ dataset = copy.deepcopy(self)
+ dataset._info.features[column] = feature
+ dataset._fingerprint = new_fingerprint
+ dataset._data = dataset._data.cast(dataset.features.arrow_schema)
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ return dataset
+ else:
+ features = self.features
+ features[column] = feature
+ return self.cast(features)
+
+ @transmit_tasks
+ @transmit_format
+ @fingerprint_transform(inplace=False)
+ def remove_columns(self, column_names: Union[str, List[str]], new_fingerprint: Optional[str] = None) -> "Dataset":
+ """
+ Remove one or several column(s) in the dataset and the features associated to them.
+
+ You can also remove a column using [`~datasets.Dataset.map`] with `remove_columns` but the present method
+ doesn't copy the data of the remaining columns and is thus faster.
+
+ Args:
+ column_names (`Union[str, List[str]]`):
+ Name of the column(s) to remove.
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset object without the columns to remove.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds = ds.remove_columns('label')
+ Dataset({
+ features: ['text'],
+ num_rows: 1066
+ })
+ >>> ds = ds.remove_columns(column_names=ds.column_names) # Removing all the columns returns an empty dataset with the `num_rows` property set to 0
+ Dataset({
+ features: [],
+ num_rows: 0
+ })
+ ```
+ """
+ dataset = copy.deepcopy(self)
+ if isinstance(column_names, str):
+ column_names = [column_names]
+
+ missing_columns = set(column_names) - set(self._data.column_names)
+ if missing_columns:
+ raise ValueError(
+ f"Column name {list(missing_columns)} not in the dataset. "
+ f"Current columns in the dataset: {dataset._data.column_names}"
+ )
+
+ for column_name in column_names:
+ del dataset._info.features[column_name]
+
+ dataset._data = dataset._data.drop(column_names)
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ dataset._fingerprint = new_fingerprint
+ return dataset
+
+ @transmit_tasks
+ @fingerprint_transform(inplace=False)
+ def rename_column(
+ self, original_column_name: str, new_column_name: str, new_fingerprint: Optional[str] = None
+ ) -> "Dataset":
+ """
+ Rename a column in the dataset, and move the features associated to the original column under the new column
+ name.
+
+ Args:
+ original_column_name (`str`):
+ Name of the column to rename.
+ new_column_name (`str`):
+ New name for the column.
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset with a renamed column.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds = ds.rename_column('label', 'label_new')
+ Dataset({
+ features: ['text', 'label_new'],
+ num_rows: 1066
+ })
+ ```
+ """
+ dataset = copy.deepcopy(self)
+ if original_column_name not in dataset._data.column_names:
+ raise ValueError(
+ f"Original column name {original_column_name} not in the dataset. "
+ f"Current columns in the dataset: {dataset._data.column_names}"
+ )
+ if new_column_name in dataset._data.column_names:
+ raise ValueError(
+ f"New column name {new_column_name} already in the dataset. "
+ f"Please choose a column name which is not already in the dataset. "
+ f"Current columns in the dataset: {dataset._data.column_names}"
+ )
+ if not new_column_name:
+ raise ValueError("New column name is empty.")
+
+ def rename(columns):
+ return [new_column_name if col == original_column_name else col for col in columns]
+
+ new_column_names = rename(self._data.column_names)
+ if self._format_columns is not None:
+ dataset._format_columns = rename(self._format_columns)
+
+ dataset._info.features = Features(
+ {
+ new_column_name if col == original_column_name else col: feature
+ for col, feature in self._info.features.items()
+ }
+ )
+
+ dataset._data = dataset._data.rename_columns(new_column_names)
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ dataset._fingerprint = new_fingerprint
+ return dataset
+
+ @transmit_tasks
+ @fingerprint_transform(inplace=False)
+ def rename_columns(self, column_mapping: Dict[str, str], new_fingerprint: Optional[str] = None) -> "Dataset":
+ """
+ Rename several columns in the dataset, and move the features associated to the original columns under
+ the new column names.
+
+ Args:
+ column_mapping (`Dict[str, str]`):
+ A mapping of columns to rename to their new names
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset with renamed columns
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds = ds.rename_columns({'text': 'text_new', 'label': 'label_new'})
+ Dataset({
+ features: ['text_new', 'label_new'],
+ num_rows: 1066
+ })
+ ```
+ """
+ dataset = copy.deepcopy(self)
+
+ extra_columns = set(column_mapping.keys()) - set(dataset.column_names)
+ if extra_columns:
+ raise ValueError(
+ f"Original column names {extra_columns} not in the dataset. "
+ f"Current columns in the dataset: {dataset._data.column_names}"
+ )
+
+ number_of_duplicates_in_new_columns = len(column_mapping.values()) - len(set(column_mapping.values()))
+ if number_of_duplicates_in_new_columns != 0:
+ raise ValueError(
+ "New column names must all be different, but this column mapping "
+ f"has {number_of_duplicates_in_new_columns} duplicates"
+ )
+
+ empty_new_columns = [new_col for new_col in column_mapping.values() if not new_col]
+ if empty_new_columns:
+ raise ValueError(f"New column names {empty_new_columns} are empty.")
+
+ def rename(columns):
+ return [column_mapping[col] if col in column_mapping else col for col in columns]
+
+ new_column_names = rename(self._data.column_names)
+ if self._format_columns is not None:
+ dataset._format_columns = rename(self._format_columns)
+
+ dataset._info.features = Features(
+ {
+ column_mapping[col] if col in column_mapping else col: feature
+ for col, feature in (self._info.features or {}).items()
+ }
+ )
+
+ dataset._data = dataset._data.rename_columns(new_column_names)
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ dataset._fingerprint = new_fingerprint
+ return dataset
+
+ @transmit_tasks
+ @transmit_format
+ @fingerprint_transform(inplace=False)
+ def select_columns(self, column_names: Union[str, List[str]], new_fingerprint: Optional[str] = None) -> "Dataset":
+ """Select one or several column(s) in the dataset and the features
+ associated to them.
+
+ Args:
+ column_names (`Union[str, List[str]]`):
+ Name of the column(s) to keep.
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform. If `None`,
+ the new fingerprint is computed using a hash of the previous
+ fingerprint, and the transform arguments.
+
+ Returns:
+ [`Dataset`]: A copy of the dataset object which only consists of
+ selected columns.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.select_columns(['text'])
+ Dataset({
+ features: ['text'],
+ num_rows: 1066
+ })
+ ```
+ """
+ if isinstance(column_names, str):
+ column_names = [column_names]
+
+ missing_columns = set(column_names) - set(self._data.column_names)
+ if missing_columns:
+ raise ValueError(
+ f"Column name {list(missing_columns)} not in the "
+ "dataset. Current columns in the dataset: "
+ f"{self._data.column_names}."
+ )
+
+ dataset = copy.deepcopy(self)
+ dataset._data = dataset._data.select(column_names)
+ dataset._info.features = Features({col: self._info.features[col] for col in dataset._data.column_names})
+ dataset._data = update_metadata_with_features(dataset._data, dataset.features)
+ dataset._fingerprint = new_fingerprint
+ return dataset
+
+ def __len__(self):
+ """Number of rows in the dataset.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.__len__
+
+ ```
+ """
+ return self.num_rows
+
+ def __iter__(self):
+ """Iterate through the examples.
+
+ If a formatting is set with [`Dataset.set_format`] rows will be returned with the
+ selected format.
+ """
+ if self._indices is None:
+ # Fast iteration
+ # Benchmark: https://gist.github.com/mariosasko/0248288a2e3a7556873969717c1fe52b (fast_iter_batch)
+ format_kwargs = self._format_kwargs if self._format_kwargs is not None else {}
+ formatter = get_formatter(self._format_type, features=self._info.features, **format_kwargs)
+ batch_size = config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER
+ for pa_subtable in table_iter(self.data, batch_size=batch_size):
+ for i in range(pa_subtable.num_rows):
+ pa_subtable_ex = pa_subtable.slice(i, 1)
+ formatted_output = format_table(
+ pa_subtable_ex,
+ 0,
+ formatter=formatter,
+ format_columns=self._format_columns,
+ output_all_columns=self._output_all_columns,
+ )
+ yield formatted_output
+ else:
+ for i in range(self.num_rows):
+ yield self._getitem(
+ i,
+ )
+
+ def iter(self, batch_size: int, drop_last_batch: bool = False):
+ """Iterate through the batches of size `batch_size`.
+
+ If a formatting is set with [`~datasets.Dataset.set_format`] rows will be returned with the
+ selected format.
+
+ Args:
+ batch_size (:obj:`int`): size of each batch to yield.
+ drop_last_batch (:obj:`bool`, default `False`): Whether a last batch smaller than the batch_size should be
+ dropped
+ """
+ if self._indices is None:
+ # Fast iteration
+ # Benchmark: https://gist.github.com/mariosasko/0248288a2e3a7556873969717c1fe52b (fast_iter_batch)
+ format_kwargs = self._format_kwargs if self._format_kwargs is not None else {}
+ formatter = get_formatter(self._format_type, features=self._info.features, **format_kwargs)
+ for pa_subtable in table_iter(self.data, batch_size=batch_size, drop_last_batch=drop_last_batch):
+ formatted_batch = format_table(
+ pa_subtable,
+ range(pa_subtable.num_rows),
+ formatter=formatter,
+ format_columns=self._format_columns,
+ output_all_columns=self._output_all_columns,
+ )
+ yield formatted_batch
+ else:
+ num_rows = self.num_rows if not drop_last_batch else self.num_rows // batch_size * batch_size
+ for i in range(0, num_rows, batch_size):
+ yield self._getitem(
+ slice(i, i + batch_size),
+ )
+
+ def __repr__(self):
+ return f"Dataset({{\n features: {list(self._info.features.keys())},\n num_rows: {self.num_rows}\n}})"
+
+ @property
+ def format(self):
+ return {
+ "type": self._format_type,
+ "format_kwargs": self._format_kwargs,
+ "columns": self.column_names if self._format_columns is None else self._format_columns,
+ "output_all_columns": self._output_all_columns,
+ }
+
+ @contextlib.contextmanager
+ def formatted_as(
+ self,
+ type: Optional[str] = None,
+ columns: Optional[List] = None,
+ output_all_columns: bool = False,
+ **format_kwargs,
+ ):
+ """To be used in a `with` statement. Set `__getitem__` return format (type and columns).
+
+ Args:
+ type (`str`, *optional*):
+ Output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`.
+ `None` means `__getitem__`` returns python objects (default).
+ columns (`List[str]`, *optional*):
+ Columns to format in the output.
+ `None` means `__getitem__` returns all columns (default).
+ output_all_columns (`bool`, defaults to `False`):
+ Keep un-formatted columns as well in the output (as python objects).
+ **format_kwargs (additional keyword arguments):
+ Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
+ """
+ old_format_type = self._format_type
+ old_format_kwargs = self._format_kwargs
+ old_format_columns = self._format_columns
+ old_output_all_columns = self._output_all_columns
+ try:
+ self.set_format(type, columns, output_all_columns, **format_kwargs)
+ yield
+ finally:
+ self.set_format(old_format_type, old_format_columns, old_output_all_columns, **old_format_kwargs)
+
+ @fingerprint_transform(inplace=True)
+ def set_format(
+ self,
+ type: Optional[str] = None,
+ columns: Optional[List] = None,
+ output_all_columns: bool = False,
+ **format_kwargs,
+ ):
+ """Set `__getitem__` return format (type and columns). The data formatting is applied on-the-fly.
+ The format `type` (for example "numpy") is used to format batches when using `__getitem__`.
+ It's also possible to use custom transforms for formatting using [`~datasets.Dataset.set_transform`].
+
+ Args:
+ type (`str`, *optional*):
+ Either output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`.
+ `None` means `__getitem__` returns python objects (default).
+ columns (`List[str]`, *optional*):
+ Columns to format in the output.
+ `None` means `__getitem__` returns all columns (default).
+ output_all_columns (`bool`, defaults to `False`):
+ Keep un-formatted columns as well in the output (as python objects).
+ **format_kwargs (additional keyword arguments):
+ Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
+
+ It is possible to call [`~datasets.Dataset.map`] after calling `set_format`. Since `map` may add new columns, then the list of formatted columns
+ gets updated. In this case, if you apply `map` on a dataset to add a new column, then this column will be formatted as:
+
+ ```
+ new formatted columns = (all columns - previously unformatted columns)
+ ```
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoTokenizer
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+ >>> ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)
+ >>> ds.set_format(type='numpy', columns=['text', 'label'])
+ >>> ds.format
+ {'type': 'numpy',
+ 'format_kwargs': {},
+ 'columns': ['text', 'label'],
+ 'output_all_columns': False}
+ ```
+ """
+ format_kwargs.update(format_kwargs.pop("format_kwargs", {})) # allow to use self.set_format(**self.format)
+
+ # Check that the format_type and format_kwargs are valid and make it possible to have a Formatter
+ type = get_format_type_from_alias(type)
+ get_formatter(type, features=self._info.features, **format_kwargs)
+
+ # Check filter column
+ if isinstance(columns, str):
+ columns = [columns]
+ if isinstance(columns, tuple):
+ columns = list(columns)
+ if columns is not None:
+ missing_columns = set(columns) - set(self._data.column_names)
+ if missing_columns:
+ raise ValueError(
+ f"Columns {list(missing_columns)} not in the dataset. Current columns in the dataset: {self._data.column_names}"
+ )
+ if columns is not None:
+ columns = columns.copy() # Ensures modifications made to the list after this call don't cause bugs
+
+ self._format_type = type
+ self._format_kwargs = format_kwargs
+ self._format_columns = columns
+ self._output_all_columns = output_all_columns
+ logger.debug(
+ "Set __getitem__(key) output type to %s for %s columns "
+ " (when key is int or slice) and %s output other (un-formatted) columns.",
+ "python objects" if type is None else type,
+ "no" if columns is None else str(columns),
+ "do" if output_all_columns else "don't",
+ )
+
+ def reset_format(self):
+ """Reset `__getitem__` return format to python objects and all columns.
+
+ Same as `self.set_format()`
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoTokenizer
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+ >>> ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)
+ >>> ds.set_format(type='numpy', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])
+ >>> ds.format
+ {'columns': ['input_ids', 'token_type_ids', 'attention_mask', 'label'],
+ 'format_kwargs': {},
+ 'output_all_columns': False,
+ 'type': 'numpy'}
+ >>> ds.reset_format()
+ >>> ds.format
+ {'columns': ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],
+ 'format_kwargs': {},
+ 'output_all_columns': False,
+ 'type': None}
+ ```
+ """
+ self.set_format()
+
+ def set_transform(
+ self,
+ transform: Optional[Callable],
+ columns: Optional[List] = None,
+ output_all_columns: bool = False,
+ ):
+ """Set `__getitem__` return format using this transform. The transform is applied on-the-fly on batches when `__getitem__` is called.
+ As [`~datasets.Dataset.set_format`], this can be reset using [`~datasets.Dataset.reset_format`].
+
+ Args:
+ transform (`Callable`, *optional*):
+ User-defined formatting transform, replaces the format defined by [`~datasets.Dataset.set_format`].
+ A formatting function is a callable that takes a batch (as a `dict`) as input and returns a batch.
+ This function is applied right before returning the objects in `__getitem__`.
+ columns (`List[str]`, *optional*):
+ Columns to format in the output.
+ If specified, then the input batch of the transform only contains those columns.
+ output_all_columns (`bool`, defaults to `False`):
+ Keep un-formatted columns as well in the output (as python objects).
+ If set to True, then the other un-formatted columns are kept with the output of the transform.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoTokenizer
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ >>> def encode(batch):
+ ... return tokenizer(batch['text'], padding=True, truncation=True, return_tensors='pt')
+ >>> ds.set_transform(encode)
+ >>> ds[0]
+ {'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1]),
+ 'input_ids': tensor([ 101, 29353, 2135, 15102, 1996, 9428, 20868, 2890, 8663, 6895,
+ 20470, 2571, 3663, 2090, 4603, 3017, 3008, 1998, 2037, 24211,
+ 5637, 1998, 11690, 2336, 1012, 102]),
+ 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0])}
+ ```
+ """
+ self.set_format("custom", columns=columns, output_all_columns=output_all_columns, transform=transform)
+
+ def with_format(
+ self,
+ type: Optional[str] = None,
+ columns: Optional[List] = None,
+ output_all_columns: bool = False,
+ **format_kwargs,
+ ):
+ """Set `__getitem__` return format (type and columns). The data formatting is applied on-the-fly.
+ The format `type` (for example "numpy") is used to format batches when using `__getitem__`.
+
+ It's also possible to use custom transforms for formatting using [`~datasets.Dataset.with_transform`].
+
+ Contrary to [`~datasets.Dataset.set_format`], `with_format` returns a new [`Dataset`] object.
+
+ Args:
+ type (`str`, *optional*):
+ Either output type selected in `[None, 'numpy', 'torch', 'tensorflow', 'pandas', 'arrow', 'jax']`.
+ `None` means `__getitem__` returns python objects (default).
+ columns (`List[str]`, *optional*):
+ Columns to format in the output.
+ `None` means `__getitem__` returns all columns (default).
+ output_all_columns (`bool`, defaults to `False`):
+ Keep un-formatted columns as well in the output (as python objects).
+ **format_kwargs (additional keyword arguments):
+ Keywords arguments passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoTokenizer
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+ >>> ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)
+ >>> ds.format
+ {'columns': ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],
+ 'format_kwargs': {},
+ 'output_all_columns': False,
+ 'type': None}
+ >>> ds = ds.with_format(type='tensorflow', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])
+ >>> ds.format
+ {'columns': ['input_ids', 'token_type_ids', 'attention_mask', 'label'],
+ 'format_kwargs': {},
+ 'output_all_columns': False,
+ 'type': 'tensorflow'}
+ ```
+ """
+ dataset = copy.deepcopy(self)
+ dataset.set_format(type=type, columns=columns, output_all_columns=output_all_columns, **format_kwargs)
+ return dataset
+
+ def with_transform(
+ self,
+ transform: Optional[Callable],
+ columns: Optional[List] = None,
+ output_all_columns: bool = False,
+ ):
+ """Set `__getitem__` return format using this transform. The transform is applied on-the-fly on batches when `__getitem__` is called.
+
+ As [`~datasets.Dataset.set_format`], this can be reset using [`~datasets.Dataset.reset_format`].
+
+ Contrary to [`~datasets.Dataset.set_transform`], `with_transform` returns a new [`Dataset`] object.
+
+ Args:
+ transform (`Callable`, `optional`):
+ User-defined formatting transform, replaces the format defined by [`~datasets.Dataset.set_format`].
+ A formatting function is a callable that takes a batch (as a `dict`) as input and returns a batch.
+ This function is applied right before returning the objects in `__getitem__`.
+ columns (`List[str]`, `optional`):
+ Columns to format in the output.
+ If specified, then the input batch of the transform only contains those columns.
+ output_all_columns (`bool`, defaults to `False`):
+ Keep un-formatted columns as well in the output (as python objects).
+ If set to `True`, then the other un-formatted columns are kept with the output of the transform.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoTokenizer
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+ >>> def encode(example):
+ ... return tokenizer(example["text"], padding=True, truncation=True, return_tensors='pt')
+ >>> ds = ds.with_transform(encode)
+ >>> ds[0]
+ {'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1]),
+ 'input_ids': tensor([ 101, 18027, 16310, 16001, 1103, 9321, 178, 11604, 7235, 6617,
+ 1742, 2165, 2820, 1206, 6588, 22572, 12937, 1811, 2153, 1105,
+ 1147, 12890, 19587, 6463, 1105, 15026, 1482, 119, 102]),
+ 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0])}
+ ```
+ """
+ dataset = copy.deepcopy(self)
+ dataset.set_transform(transform=transform, columns=columns, output_all_columns=output_all_columns)
+ return dataset
+
+ @deprecated()
+ def prepare_for_task(self, task: Union[str, TaskTemplate], id: int = 0) -> "Dataset":
+ """
+ Prepare a dataset for the given task by casting the dataset's [`Features`] to standardized column names and types as detailed in [`datasets.tasks`](./task_templates).
+
+ Casts [`datasets.DatasetInfo.features`] according to a task-specific schema. Intended for single-use only, so all task templates are removed from [`datasets.DatasetInfo.task_templates`] after casting.
+
+ Args:
+ task (`Union[str, TaskTemplate]`):
+ The task to prepare the dataset for during training and evaluation. If `str`, supported tasks include:
+
+ - `"text-classification"`
+ - `"question-answering"`
+
+ If [`TaskTemplate`], must be one of the task templates in [`datasets.tasks`](./task_templates).
+ id (`int`, defaults to `0`):
+ The id required to unambiguously identify the task template when multiple task templates of the same type are supported.
+ """
+ # TODO(lewtun): Add support for casting nested features like answers.text and answers.answer_start in SQuAD
+ if isinstance(task, str):
+ tasks = [template.task for template in (self.info.task_templates or [])]
+ compatible_templates = [template for template in (self.info.task_templates or []) if template.task == task]
+ if not compatible_templates:
+ raise ValueError(
+ f"Task {task} is not compatible with this dataset! Available tasks: {list(unique_values(tasks))}"
+ )
+
+ if not 0 <= id < len(compatible_templates):
+ templates_list_str = "\n".join(
+ f"- `{idx}` for task {template}" for idx, template in enumerate(compatible_templates)
+ )
+ raise ValueError(
+ f"Id {id} for task {task} is not in a valid range. Supported ids:\n{templates_list_str}"
+ )
+ template = compatible_templates[id]
+ elif isinstance(task, TaskTemplate):
+ template = task
+ else:
+ raise ValueError(
+ f"Expected a `str` or `datasets.TaskTemplate` object but got task {task} with type {type(task)}."
+ )
+ template = template.align_with_features(self.info.features)
+ column_mapping = template.column_mapping
+ columns_to_drop = [column for column in self.column_names if column not in column_mapping]
+ dataset = self.remove_columns(columns_to_drop)
+ dataset = dataset.rename_columns(column_mapping)
+ # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`
+ dataset.info.task_templates = None
+ dataset = dataset.cast(features=template.features)
+ return dataset
+
+ def _getitem(self, key: Union[int, slice, str, ListLike[int]], **kwargs) -> Union[Dict, List]:
+ """
+ Can be used to index columns (by string names) or rows (by integer, slice, or list-like of integer indices)
+ """
+ if isinstance(key, bool):
+ raise TypeError("dataset index must be int, str, slice or collection of int, not bool")
+ format_type = kwargs["format_type"] if "format_type" in kwargs else self._format_type
+ format_columns = kwargs["format_columns"] if "format_columns" in kwargs else self._format_columns
+ output_all_columns = (
+ kwargs["output_all_columns"] if "output_all_columns" in kwargs else self._output_all_columns
+ )
+ format_kwargs = kwargs["format_kwargs"] if "format_kwargs" in kwargs else self._format_kwargs
+ format_kwargs = format_kwargs if format_kwargs is not None else {}
+ formatter = get_formatter(format_type, features=self._info.features, **format_kwargs)
+ pa_subtable = query_table(self._data, key, indices=self._indices)
+ formatted_output = format_table(
+ pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
+ )
+ return formatted_output
+
+ @overload
+ def __getitem__(self, key: Union[int, slice, Iterable[int]]) -> Dict: # noqa: F811
+ ...
+
+ @overload
+ def __getitem__(self, key: str) -> List: # noqa: F811
+ ...
+
+ def __getitem__(self, key): # noqa: F811
+ """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
+ return self._getitem(key)
+
+ def __getitems__(self, keys: List) -> List:
+ """Can be used to get a batch using a list of integers indices."""
+ batch = self.__getitem__(keys)
+ n_examples = len(batch[next(iter(batch))])
+ return [{col: array[i] for col, array in batch.items()} for i in range(n_examples)]
+
+ def cleanup_cache_files(self) -> int:
+ """Clean up all cache files in the dataset cache directory, excepted the currently used cache file if there is
+ one.
+
+ Be careful when running this command that no other process is currently using other cache files.
+
+ Returns:
+ `int`: Number of removed files.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.cleanup_cache_files()
+ 10
+ ```
+ """
+ current_cache_files = [os.path.abspath(cache_file["filename"]) for cache_file in self.cache_files]
+ if not current_cache_files:
+ return 0
+ cache_directory = os.path.dirname(current_cache_files[0])
+ logger.info(f"Listing files in {cache_directory}")
+ files: List[str] = os.listdir(cache_directory)
+ files_to_remove = []
+ for f_name in files:
+ full_name = os.path.abspath(os.path.join(cache_directory, f_name))
+ if f_name.startswith("cache-") and f_name.endswith(".arrow"):
+ if full_name in current_cache_files:
+ logger.info(f"Keeping currently used cache file at {full_name}")
+ continue
+ files_to_remove.append(full_name)
+ for file_path in files_to_remove:
+ logger.info(f"Removing {file_path}")
+ os.remove(file_path)
+ return len(files_to_remove)
+
+ def _get_cache_file_path(self, fingerprint):
+ if is_caching_enabled() and self.cache_files:
+ cache_file_name = "cache-" + fingerprint + ".arrow"
+ cache_directory = os.path.dirname(self.cache_files[0]["filename"])
+ else:
+ cache_file_name = "cache-" + generate_random_fingerprint() + ".arrow"
+ cache_directory = get_temporary_cache_files_directory()
+ cache_file_path = os.path.join(cache_directory, cache_file_name)
+ return cache_file_path
+
+ @transmit_tasks
+ @transmit_format
+ def map(
+ self,
+ function: Optional[Callable] = None,
+ with_indices: bool = False,
+ with_rank: bool = False,
+ input_columns: Optional[Union[str, List[str]]] = None,
+ batched: bool = False,
+ batch_size: Optional[int] = 1000,
+ drop_last_batch: bool = False,
+ remove_columns: Optional[Union[str, List[str]]] = None,
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ features: Optional[Features] = None,
+ disable_nullable: bool = False,
+ fn_kwargs: Optional[dict] = None,
+ num_proc: Optional[int] = None,
+ suffix_template: str = "_{rank:05d}_of_{num_proc:05d}",
+ new_fingerprint: Optional[str] = None,
+ desc: Optional[str] = None,
+ ) -> "Dataset":
+ """
+ Apply a function to all the examples in the table (individually or in batches) and update the table.
+ If your function returns a column that already exists, then it overwrites it.
+
+ You can specify whether the function should be batched or not with the `batched` parameter:
+
+ - If batched is `False`, then the function takes 1 example in and should return 1 example.
+ An example is a dictionary, e.g. `{"text": "Hello there !"}`.
+ - If batched is `True` and `batch_size` is 1, then the function takes a batch of 1 example as input and can return a batch with 1 or more examples.
+ A batch is a dictionary, e.g. a batch of 1 example is `{"text": ["Hello there !"]}`.
+ - If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.
+ Note that the last batch may have less than `n` examples.
+ A batch is a dictionary, e.g. a batch of `n` examples is `{"text": ["Hello there !"] * n}`.
+
+ Args:
+ function (`Callable`): Function with one of the following signatures:
+
+ - `function(example: Dict[str, Any]) -> Dict[str, Any]` if `batched=False` and `with_indices=False` and `with_rank=False`
+ - `function(example: Dict[str, Any], *extra_args) -> Dict[str, Any]` if `batched=False` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+ - `function(batch: Dict[str, List]) -> Dict[str, List]` if `batched=True` and `with_indices=False` and `with_rank=False`
+ - `function(batch: Dict[str, List], *extra_args) -> Dict[str, List]` if `batched=True` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+
+ For advanced usage, the function can also return a `pyarrow.Table`.
+ Moreover if your function returns nothing (`None`), then `map` will run your function and return the dataset unchanged.
+ If no function is provided, default to identity function: `lambda x: x`.
+ with_indices (`bool`, defaults to `False`):
+ Provide example indices to `function`. Note that in this case the
+ signature of `function` should be `def function(example, idx[, rank]): ...`.
+ with_rank (`bool`, defaults to `False`):
+ Provide process rank to `function`. Note that in this case the
+ signature of `function` should be `def function(example[, idx], rank): ...`.
+ input_columns (`Optional[Union[str, List[str]]]`, defaults to `None`):
+ The columns to be passed into `function`
+ as positional arguments. If `None`, a `dict` mapping to all formatted columns is passed as one argument.
+ batched (`bool`, defaults to `False`):
+ Provide batch of examples to `function`.
+ batch_size (`int`, *optional*, defaults to `1000`):
+ Number of examples per batch provided to `function` if `batched=True`.
+ If `batch_size <= 0` or `batch_size == None`, provide the full dataset as a single batch to `function`.
+ drop_last_batch (`bool`, defaults to `False`):
+ Whether a last batch smaller than the batch_size should be
+ dropped instead of being processed by the function.
+ remove_columns (`Optional[Union[str, List[str]]]`, defaults to `None`):
+ Remove a selection of columns while doing the mapping.
+ Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding
+ columns with names in `remove_columns`, these columns will be kept.
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the dataset in memory instead of writing it to a cache file.
+ load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
+ If a cache file storing the current computation from `function`
+ can be identified, use it instead of recomputing.
+ cache_file_name (`str`, *optional*, defaults to `None`):
+ Provide the name of a path for the cache file. It is used to store the
+ results of the computation instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ features (`Optional[datasets.Features]`, defaults to `None`):
+ Use a specific Features to store the cache file
+ instead of the automatically generated one.
+ disable_nullable (`bool`, defaults to `False`):
+ Disallow null values in the table.
+ fn_kwargs (`Dict`, *optional*, defaults to `None`):
+ Keyword arguments to be passed to `function`.
+ num_proc (`int`, *optional*, defaults to `None`):
+ Max number of processes when generating cache. Already cached shards are loaded sequentially.
+ suffix_template (`str`):
+ If `cache_file_name` is specified, then this suffix
+ will be added at the end of the base name of each. Defaults to `"_{rank:05d}_of_{num_proc:05d}"`. For example, if `cache_file_name` is "processed.arrow", then for
+ `rank=1` and `num_proc=4`, the resulting file would be `"processed_00001_of_00004.arrow"` for the default suffix.
+ new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+ desc (`str`, *optional*, defaults to `None`):
+ Meaningful description to be displayed alongside with the progress bar while mapping examples.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> def add_prefix(example):
+ ... example["text"] = "Review: " + example["text"]
+ ... return example
+ >>> ds = ds.map(add_prefix)
+ >>> ds[0:3]["text"]
+ ['Review: compassionately explores the seemingly irreconcilable situation between conservative christian parents and their estranged gay and lesbian children .',
+ 'Review: the soundtrack alone is worth the price of admission .',
+ 'Review: rodriguez does a splendid job of racial profiling hollywood style--casting excellent latin actors of all ages--a trend long overdue .']
+
+ # process a batch of examples
+ >>> ds = ds.map(lambda example: tokenizer(example["text"]), batched=True)
+ # set number of processors
+ >>> ds = ds.map(add_prefix, num_proc=4)
+ ```
+ """
+ if keep_in_memory and cache_file_name is not None:
+ raise ValueError("Please use either `keep_in_memory` or `cache_file_name` but not both.")
+
+ if num_proc is not None and num_proc <= 0:
+ raise ValueError("num_proc must be an integer > 0.")
+
+ # If the array is empty we do nothing (but we make sure to handle an empty indices mapping and remove the requested columns anyway)
+ if len(self) == 0:
+ if self._indices is not None: # empty indices mapping
+ self = Dataset(
+ self.data.slice(0, 0),
+ info=self.info.copy(),
+ split=self.split,
+ fingerprint=new_fingerprint,
+ )
+ if remove_columns:
+ return self.remove_columns(remove_columns)
+ else:
+ return self
+
+ if function is None:
+ function = lambda x: x # noqa: E731
+
+ if isinstance(input_columns, str):
+ input_columns = [input_columns]
+
+ if input_columns is not None:
+ missing_columns = set(input_columns) - set(self._data.column_names)
+ if missing_columns:
+ raise ValueError(
+ f"Input column {list(missing_columns)} not in the dataset. Current columns in the dataset: {self._data.column_names}"
+ )
+
+ if isinstance(remove_columns, str):
+ remove_columns = [remove_columns]
+
+ if remove_columns is not None:
+ missing_columns = set(remove_columns) - set(self._data.column_names)
+ if missing_columns:
+ raise ValueError(
+ f"Column to remove {list(missing_columns)} not in the dataset. Current columns in the dataset: {self._data.column_names}"
+ )
+
+ load_from_cache_file = load_from_cache_file if load_from_cache_file is not None else is_caching_enabled()
+
+ if fn_kwargs is None:
+ fn_kwargs = {}
+
+ if num_proc is not None and num_proc > len(self):
+ num_proc = len(self)
+ logger.warning(
+ f"num_proc must be <= {len(self)}. Reducing num_proc to {num_proc} for dataset of size {len(self)}."
+ )
+
+ dataset_kwargs = {
+ "shard": self,
+ "function": function,
+ "with_indices": with_indices,
+ "with_rank": with_rank,
+ "input_columns": input_columns,
+ "batched": batched,
+ "batch_size": batch_size,
+ "drop_last_batch": drop_last_batch,
+ "remove_columns": remove_columns,
+ "keep_in_memory": keep_in_memory,
+ "writer_batch_size": writer_batch_size,
+ "features": features,
+ "disable_nullable": disable_nullable,
+ "fn_kwargs": fn_kwargs,
+ }
+
+ if new_fingerprint is None:
+ # we create a unique hash from the function,
+ # current dataset file and the mapping args
+ transform = format_transform_for_fingerprint(Dataset._map_single)
+ kwargs_for_fingerprint = format_kwargs_for_fingerprint(Dataset._map_single, (), dataset_kwargs)
+ kwargs_for_fingerprint["fingerprint_name"] = "new_fingerprint"
+ new_fingerprint = update_fingerprint(self._fingerprint, transform, kwargs_for_fingerprint)
+ else:
+ validate_fingerprint(new_fingerprint)
+ dataset_kwargs["new_fingerprint"] = new_fingerprint
+
+ if self.cache_files:
+ if cache_file_name is None:
+ cache_file_name = self._get_cache_file_path(new_fingerprint)
+ dataset_kwargs["cache_file_name"] = cache_file_name
+
+ def load_processed_shard_from_cache(shard_kwargs):
+ """Load a processed shard from cache if it exists, otherwise throw an error."""
+ shard = shard_kwargs["shard"]
+ # Check if we've already cached this computation (indexed by a hash)
+ if shard_kwargs["cache_file_name"] is not None:
+ if os.path.exists(shard_kwargs["cache_file_name"]) and load_from_cache_file:
+ info = shard.info.copy()
+ info.features = features
+ info.task_templates = None
+ return Dataset.from_file(shard_kwargs["cache_file_name"], info=info, split=shard.split)
+ raise NonExistentDatasetError
+
+ num_shards = num_proc if num_proc is not None else 1
+ if batched and drop_last_batch:
+ pbar_total = len(self) // num_shards // batch_size * num_shards * batch_size
+ else:
+ pbar_total = len(self)
+
+ shards_done = 0
+ if num_proc is None or num_proc == 1:
+ transformed_dataset = None
+ try:
+ transformed_dataset = load_processed_shard_from_cache(dataset_kwargs)
+ logger.info(f"Loading cached processed dataset at {dataset_kwargs['cache_file_name']}")
+ except NonExistentDatasetError:
+ pass
+ if transformed_dataset is None:
+ with hf_tqdm(
+ unit=" examples",
+ total=pbar_total,
+ desc=desc or "Map",
+ ) as pbar:
+ for rank, done, content in Dataset._map_single(**dataset_kwargs):
+ if done:
+ shards_done += 1
+ logger.debug(f"Finished processing shard number {rank} of {num_shards}.")
+ transformed_dataset = content
+ else:
+ pbar.update(content)
+ assert transformed_dataset is not None, "Failed to retrieve the result from map"
+ # update fingerprint if the dataset changed
+ if transformed_dataset._fingerprint != self._fingerprint:
+ transformed_dataset._fingerprint = new_fingerprint
+ return transformed_dataset
+ else:
+
+ def format_cache_file_name(
+ cache_file_name: Optional[str],
+ rank: Union[int, Literal["*"]], # noqa: F722
+ ) -> Optional[str]:
+ if not cache_file_name:
+ return cache_file_name
+ sep = cache_file_name.rindex(".")
+ base_name, extension = cache_file_name[:sep], cache_file_name[sep:]
+ if isinstance(rank, int):
+ cache_file_name = base_name + suffix_template.format(rank=rank, num_proc=num_proc) + extension
+ logger.info(f"Process #{rank} will write at {cache_file_name}")
+ else:
+ cache_file_name = (
+ base_name
+ + suffix_template.replace("{rank:05d}", "{rank}").format(rank=rank, num_proc=num_proc)
+ + extension
+ )
+ return cache_file_name
+
+ def format_new_fingerprint(new_fingerprint: str, rank: int) -> str:
+ new_fingerprint = new_fingerprint + suffix_template.format(rank=rank, num_proc=num_proc)
+ validate_fingerprint(new_fingerprint)
+ return new_fingerprint
+
+ prev_env = deepcopy(os.environ)
+ # check if parallelism if off
+ # from https://github.com/huggingface/tokenizers/blob/bb668bc439dc34389b71dbb8ce0c597f15707b53/tokenizers/src/utils/parallelism.rs#L22
+ if prev_env.get("TOKENIZERS_PARALLELISM", "false").lower() not in (
+ "",
+ "off",
+ "false",
+ "f",
+ "no",
+ "n",
+ "0",
+ ):
+ logger.warning("Setting TOKENIZERS_PARALLELISM=false for forked processes.")
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
+ shards = [
+ self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory)
+ for rank in range(num_proc)
+ ]
+ kwargs_per_job = [
+ {
+ **dataset_kwargs,
+ "shard": shards[rank],
+ "cache_file_name": format_cache_file_name(cache_file_name, rank),
+ "rank": rank,
+ "offset": sum(len(s) for s in shards[:rank]),
+ "new_fingerprint": format_new_fingerprint(new_fingerprint, rank),
+ }
+ for rank in range(num_shards)
+ ]
+
+ transformed_shards = [None] * num_shards
+ for rank in range(num_shards):
+ try:
+ transformed_shards[rank] = load_processed_shard_from_cache(kwargs_per_job[rank])
+ kwargs_per_job[rank] = None
+ except NonExistentDatasetError:
+ pass
+
+ kwargs_per_job = [kwargs for kwargs in kwargs_per_job if kwargs is not None]
+
+ # We try to create a pool with as many workers as dataset not yet cached.
+ if kwargs_per_job:
+ if len(kwargs_per_job) < num_shards:
+ logger.info(
+ f"Reprocessing {len(kwargs_per_job)}/{num_shards} shards because some of them were missing from the cache."
+ )
+ with Pool(len(kwargs_per_job)) as pool:
+ os.environ = prev_env
+ logger.info(f"Spawning {num_proc} processes")
+ with hf_tqdm(
+ unit=" examples",
+ total=pbar_total,
+ desc=(desc or "Map") + f" (num_proc={num_proc})",
+ ) as pbar:
+ for rank, done, content in iflatmap_unordered(
+ pool, Dataset._map_single, kwargs_iterable=kwargs_per_job
+ ):
+ if done:
+ shards_done += 1
+ logger.debug(f"Finished processing shard number {rank} of {num_shards}.")
+ transformed_shards[rank] = content
+ else:
+ pbar.update(content)
+ # Avoids PermissionError on Windows (the error: https://github.com/huggingface/datasets/actions/runs/4026734820/jobs/6921621805)
+ for kwargs in kwargs_per_job:
+ del kwargs["shard"]
+ else:
+ logger.info(f"Loading cached processed dataset at {format_cache_file_name(cache_file_name, '*')}")
+ assert (
+ None not in transformed_shards
+ ), f"Failed to retrieve results from map: result list {transformed_shards} still contains None - at least one worker failed to return its results"
+ logger.info(f"Concatenating {num_proc} shards")
+ result = _concatenate_map_style_datasets(transformed_shards)
+ # update fingerprint if the dataset changed
+ if any(
+ transformed_shard._fingerprint != shard._fingerprint
+ for transformed_shard, shard in zip(transformed_shards, shards)
+ ):
+ result._fingerprint = new_fingerprint
+ else:
+ result._fingerprint = self._fingerprint
+ return result
+
+ @staticmethod
+ def _map_single(
+ shard: "Dataset",
+ function: Optional[Callable] = None,
+ with_indices: bool = False,
+ with_rank: bool = False,
+ input_columns: Optional[List[str]] = None,
+ batched: bool = False,
+ batch_size: Optional[int] = 1000,
+ drop_last_batch: bool = False,
+ remove_columns: Optional[List[str]] = None,
+ keep_in_memory: bool = False,
+ cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ features: Optional[Features] = None,
+ disable_nullable: bool = False,
+ fn_kwargs: Optional[dict] = None,
+ new_fingerprint: Optional[str] = None,
+ rank: Optional[int] = None,
+ offset: int = 0,
+ ) -> Iterable[Tuple[int, bool, Union[int, "Dataset"]]]:
+ """Apply a function to all the elements in the table (individually or in batches)
+ and update the table (if function does update examples).
+
+ Args:
+ shard (`datasets.Dataset`): Dataset to map the transform on.
+ function (`Callable`): with one of the following signature:
+ - `function(example: Dict[str, Any]) -> Dict[str, Any]` if `batched=False` and `with_indices=False` and `with_rank=False`
+ - `function(example: Dict[str, Any], *extra_args) -> Dict[str, Any]` if `batched=False` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+ - `function(batch: Dict[str, List]) -> Dict[str, List]` if `batched=True` and `with_indices=False` and `with_rank=False`
+ - `function(batch: Dict[str, List], *extra_args) -> Dict[str, List]` if `batched=True` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+
+ For advanced usage, the function can also return a `pyarrow.Table`.
+ Moreover if your function returns nothing (`None`), then `map` will run your function and return the dataset unchanged.
+ If no function is provided, default to identity function: lambda x: x
+ with_indices (`bool`, defaults to `False`): Provide example indices to `function`. Note that in this case the signature of `function` should be `def function(example, idx[, rank]): ...`.
+ with_rank (`bool`, default `False`): Provide process rank to `function`. Note that in this case the signature of `function` should be `def function(example[, idx], rank): ...`.
+ input_columns (`Optional[List[str]]`, defaults to `None`): The columns to be passed into `function` as
+ positional arguments. If `None`, a dict mapping to all formatted columns is passed as one argument.
+ batched (`bool`, defaults to `False`): Provide batch of examples to `function`
+ batch_size (`int`, optional, defaults to `1000`): Number of examples per batch provided to `function` if `batched=True`
+ `batch_size <= 0` or `batch_size == None`: Provide the full dataset as a single batch to `function`
+ drop_last_batch (`bool`, default: `False`): Whether a last batch smaller than the batch_size should be
+ dropped instead of being processed by the function.
+ remove_columns (`Optional[List[str]]`, defaults to `None`): Remove a selection of columns while doing the mapping.
+ Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding
+ columns with names in `remove_columns`, these columns will be kept.
+ keep_in_memory (`bool`, defaults to `False`): Keep the dataset in memory instead of writing it to a cache file.
+ cache_file_name (`str`, optional, defaults to `None`): Provide the name of a path for the cache file. It is used to store the
+ results of the computation instead of the automatically generated cache file name.
+ writer_batch_size (`int`, default `1000`): Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `.map()`.
+ features (`Optional[datasets.Features]`, defaults to `None`): Use a specific Features to store the cache file
+ instead of the automatically generated one.
+ disable_nullable (`bool`, defaults to `False`): Disallow null values in the table.
+ fn_kwargs (`Dict`, optional, defaults to `None`): Keyword arguments to be passed to `function`
+ new_fingerprint (`str`, optional, defaults to `None`): the new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+ rank: (`int`, optional, defaults to `None`): If specified, this is the process rank when doing multiprocessing
+ offset: (`int`, defaults to 0): If specified, this is an offset applied to the indices passed to `function` if `with_indices=True`.
+ """
+ if fn_kwargs is None:
+ fn_kwargs = {}
+
+ # If we do batch computation but no batch size is provided, default to the full dataset
+ if batched and (batch_size is None or batch_size <= 0):
+ batch_size = shard.num_rows
+
+ # We set this variable to True after processing the first example/batch in
+ # `apply_function_on_filtered_inputs` if the map function returns a dict.
+ # If set to False, no new arrow table will be created
+
+ update_data = None
+
+ format_kwargs = shard._format_kwargs.copy()
+ # Lazy formatting is only available for the default format (None/python)
+ if not input_columns and shard._format_type is None:
+ format_kwargs["lazy"] = True
+ input_formatter = get_formatter(
+ shard._format_type,
+ features=shard.features,
+ **format_kwargs,
+ )
+
+ class NumExamplesMismatchError(Exception):
+ pass
+
+ def validate_function_output(processed_inputs, indices):
+ """Validate output of the map function."""
+ if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table, pd.DataFrame)):
+ raise TypeError(
+ f"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects."
+ )
+ elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):
+ allowed_batch_return_types = (list, np.ndarray, pd.Series)
+ if config.POLARS_AVAILABLE and "polars" in sys.modules:
+ import polars as pl
+
+ allowed_batch_return_types += (pl.Series, pl.DataFrame)
+ if config.TF_AVAILABLE and "tensorflow" in sys.modules:
+ import tensorflow as tf
+
+ allowed_batch_return_types += (tf.Tensor,)
+ if config.TORCH_AVAILABLE and "torch" in sys.modules:
+ import torch
+
+ allowed_batch_return_types += (torch.Tensor,)
+ if config.JAX_AVAILABLE and "jax" in sys.modules:
+ import jax.numpy as jnp
+
+ allowed_batch_return_types += (jnp.ndarray,)
+ all_dict_values_are_lists = all(
+ isinstance(value, allowed_batch_return_types) for value in processed_inputs.values()
+ )
+ if all_dict_values_are_lists is False:
+ raise TypeError(
+ f"Provided `function` which is applied to all elements of table returns a `dict` of types {[type(x) for x in processed_inputs.values()]}. When using `batched=True`, make sure provided `function` returns a `dict` of types like `{allowed_batch_return_types}`."
+ )
+
+ def apply_function_on_filtered_inputs(pa_inputs, indices, check_same_num_examples=False, offset=0):
+ """Utility to apply the function on a selection of columns."""
+ nonlocal update_data
+ inputs = format_table(
+ pa_inputs,
+ 0 if not batched else range(pa_inputs.num_rows),
+ format_columns=input_columns,
+ formatter=input_formatter,
+ )
+ fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
+ if offset == 0:
+ effective_indices = indices
+ else:
+ effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
+ additional_args = ()
+ if with_indices:
+ additional_args += (effective_indices,)
+ if with_rank:
+ additional_args += (rank,)
+ processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
+ if isinstance(processed_inputs, LazyDict):
+ processed_inputs = {
+ k: v for k, v in processed_inputs.data.items() if k not in processed_inputs.keys_to_format
+ }
+ returned_lazy_dict = True
+ else:
+ returned_lazy_dict = False
+ if update_data is None:
+ # Check if the function returns updated examples
+ update_data = isinstance(processed_inputs, (Mapping, pa.Table, pd.DataFrame))
+ validate_function_output(processed_inputs, indices)
+ if not update_data:
+ return None # Nothing to update, let's move on
+ if shard._format_type or input_columns:
+ # TODO(QL, MS): ideally the behavior should be the same even if the dataset is formatted (may require major release)
+ inputs_to_merge = dict(zip(pa_inputs.column_names, pa_inputs.itercolumns()))
+ elif isinstance(inputs, LazyDict):
+ inputs_to_merge = {
+ k: (v if k not in inputs.keys_to_format else pa_inputs[k]) for k, v in inputs.data.items()
+ }
+ else:
+ inputs_to_merge = inputs
+ if remove_columns is not None:
+ for column in remove_columns:
+ # `function` can modify input in-place causing column to be already removed.
+ if column in inputs_to_merge:
+ inputs_to_merge.pop(column)
+ if returned_lazy_dict and column in processed_inputs:
+ processed_inputs.pop(column)
+ if check_same_num_examples:
+ input_num_examples = len(pa_inputs)
+ processed_inputs_num_examples = len(processed_inputs[next(iter(processed_inputs.keys()))])
+ if input_num_examples != processed_inputs_num_examples:
+ raise NumExamplesMismatchError()
+ if isinstance(inputs, Mapping) and isinstance(processed_inputs, Mapping):
+ # The .map() transform *updates* the dataset:
+ # the output dictionary contains both the the input data and the output data.
+ # The output dictionary may contain Arrow values from `inputs_to_merge` so that we can re-write them efficiently.
+ return {**inputs_to_merge, **processed_inputs}
+ else:
+ return processed_inputs
+
+ def init_buffer_and_writer():
+ # Prepare output buffer and batched writer in memory or on file if we update the table
+ writer_features = features
+ if writer_features is None:
+ writer_features = shard.features
+ update_features = True
+ else:
+ update_features = False
+ if keep_in_memory or cache_file_name is None:
+ buf_writer = pa.BufferOutputStream()
+ tmp_file = None
+ writer = ArrowWriter(
+ features=writer_features,
+ stream=buf_writer,
+ writer_batch_size=writer_batch_size,
+ update_features=update_features,
+ fingerprint=new_fingerprint,
+ disable_nullable=disable_nullable,
+ )
+ else:
+ buf_writer = None
+ logger.info(f"Caching processed dataset at {cache_file_name}")
+ tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
+ writer = ArrowWriter(
+ features=writer_features,
+ path=tmp_file.name,
+ writer_batch_size=writer_batch_size,
+ update_features=update_features,
+ fingerprint=new_fingerprint,
+ disable_nullable=disable_nullable,
+ )
+ return buf_writer, writer, tmp_file
+
+ num_examples_progress_update = 0
+ # If `update_data` is True after processing the first example/batch, initalize these resources with `init_buffer_and_writer`
+ buf_writer, writer, tmp_file = None, None, None
+
+ # Check if Polars is available and import it if so
+ if config.POLARS_AVAILABLE and "polars" in sys.modules:
+ import polars as pl
+
+ # Optionally initialize the writer as a context manager
+ with contextlib.ExitStack() as stack:
+ try:
+ arrow_formatted_shard = shard.with_format("arrow")
+
+ # Loop over single examples or batches and write to buffer/file if examples are to be updated
+ if not batched:
+ shard_iterable = enumerate(arrow_formatted_shard)
+ else:
+ num_rows = len(shard) if not drop_last_batch else len(shard) // batch_size * batch_size
+ shard_iterable = zip(
+ range(0, num_rows, batch_size),
+ arrow_formatted_shard.iter(batch_size, drop_last_batch=drop_last_batch),
+ )
+ if not batched:
+ _time = time.time()
+ for i, example in shard_iterable:
+ example = apply_function_on_filtered_inputs(example, i, offset=offset)
+ if update_data:
+ if i == 0:
+ buf_writer, writer, tmp_file = init_buffer_and_writer()
+ stack.enter_context(writer)
+ if isinstance(example, pa.Table):
+ writer.write_row(example)
+ elif isinstance(example, pd.DataFrame):
+ writer.write_row(pa.Table.from_pandas(example))
+ elif (
+ config.POLARS_AVAILABLE
+ and "polars" in sys.modules
+ and isinstance(example, pl.DataFrame)
+ ):
+ writer.write_row(example.to_arrow())
+ else:
+ writer.write(example)
+ num_examples_progress_update += 1
+ if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
+ _time = time.time()
+ yield rank, False, num_examples_progress_update
+ num_examples_progress_update = 0
+ else:
+ _time = time.time()
+ for i, batch in shard_iterable:
+ num_examples_in_batch = len(batch)
+ indices = list(
+ range(*(slice(i, i + batch_size).indices(shard.num_rows)))
+ ) # Something simpler?
+ try:
+ batch = apply_function_on_filtered_inputs(
+ batch,
+ indices,
+ check_same_num_examples=len(shard.list_indexes()) > 0,
+ offset=offset,
+ )
+ except NumExamplesMismatchError:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."
+ ) from None
+ if update_data:
+ if i == 0:
+ buf_writer, writer, tmp_file = init_buffer_and_writer()
+ stack.enter_context(writer)
+ if isinstance(batch, pa.Table):
+ writer.write_table(batch)
+ elif isinstance(batch, pd.DataFrame):
+ writer.write_table(pa.Table.from_pandas(batch))
+ elif (
+ config.POLARS_AVAILABLE and "polars" in sys.modules and isinstance(batch, pl.DataFrame)
+ ):
+ writer.write_table(batch.to_arrow())
+ else:
+ writer.write_batch(batch)
+ num_examples_progress_update += num_examples_in_batch
+ if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
+ _time = time.time()
+ yield rank, False, num_examples_progress_update
+ num_examples_progress_update = 0
+ if update_data and writer is not None:
+ writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
+ except (Exception, KeyboardInterrupt):
+ yield rank, False, num_examples_progress_update
+ if update_data:
+ if writer is not None:
+ writer.finalize()
+ if tmp_file is not None:
+ tmp_file.close()
+ if os.path.exists(tmp_file.name):
+ os.remove(tmp_file.name)
+ raise
+
+ yield rank, False, num_examples_progress_update
+ if update_data and tmp_file is not None:
+ tmp_file.close()
+ shutil.move(tmp_file.name, cache_file_name)
+ umask = os.umask(0o666)
+ os.umask(umask)
+ os.chmod(cache_file_name, 0o666 & ~umask)
+
+ if update_data:
+ # Create new Dataset from buffer or file
+ info = shard.info.copy()
+ info.features = writer._features
+ info.task_templates = None
+ if buf_writer is None:
+ yield rank, True, Dataset.from_file(cache_file_name, info=info, split=shard.split)
+ else:
+ yield rank, True, Dataset.from_buffer(buf_writer.getvalue(), info=info, split=shard.split)
+ else:
+ yield rank, True, shard
+
+ @transmit_format
+ @fingerprint_transform(
+ inplace=False, ignore_kwargs=["load_from_cache_file", "cache_file_name", "desc"], version="2.0.1"
+ )
+ def filter(
+ self,
+ function: Optional[Callable] = None,
+ with_indices: bool = False,
+ with_rank: bool = False,
+ input_columns: Optional[Union[str, List[str]]] = None,
+ batched: bool = False,
+ batch_size: Optional[int] = 1000,
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ fn_kwargs: Optional[dict] = None,
+ num_proc: Optional[int] = None,
+ suffix_template: str = "_{rank:05d}_of_{num_proc:05d}",
+ new_fingerprint: Optional[str] = None,
+ desc: Optional[str] = None,
+ ) -> "Dataset":
+ """Apply a filter function to all the elements in the table in batches
+ and update the table so that the dataset only includes examples according to the filter function.
+
+ Args:
+ function (`Callable`): Callable with one of the following signatures:
+
+ - `function(example: Dict[str, Any]) -> bool` if `batched=False` and `with_indices=False` and `with_rank=False`
+ - `function(example: Dict[str, Any], *extra_args) -> bool` if `batched=False` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+ - `function(batch: Dict[str, List]) -> List[bool]` if `batched=True` and `with_indices=False` and `with_rank=False`
+ - `function(batch: Dict[str, List], *extra_args) -> List[bool]` if `batched=True` and `with_indices=True` and/or `with_rank=True` (one extra arg for each)
+
+ If no function is provided, defaults to an always `True` function: `lambda x: True`.
+ with_indices (`bool`, defaults to `False`):
+ Provide example indices to `function`. Note that in this case the
+ signature of `function` should be `def function(example, idx[, rank]): ...`.
+ with_rank (`bool`, defaults to `False`):
+ Provide process rank to `function`. Note that in this case the
+ signature of `function` should be `def function(example[, idx], rank): ...`.
+ input_columns (`str` or `List[str]`, *optional*):
+ The columns to be passed into `function` as
+ positional arguments. If `None`, a `dict` mapping to all formatted columns is passed as one argument.
+ batched (`bool`, defaults to `False`):
+ Provide batch of examples to `function`.
+ batch_size (`int`, *optional*, defaults to `1000`):
+ Number of examples per batch provided to `function` if
+ `batched = True`. If `batched = False`, one example per batch is passed to `function`.
+ If `batch_size <= 0` or `batch_size == None`, provide the full dataset as a single batch to `function`.
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the dataset in memory instead of writing it to a cache file.
+ load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
+ If a cache file storing the current computation from `function`
+ can be identified, use it instead of recomputing.
+ cache_file_name (`str`, *optional*):
+ Provide the name of a path for the cache file. It is used to store the
+ results of the computation instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ fn_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to `function`.
+ num_proc (`int`, *optional*):
+ Number of processes for multiprocessing. By default it doesn't
+ use multiprocessing.
+ suffix_template (`str`):
+ If `cache_file_name` is specified, then this suffix will be added at the end of the base name of each.
+ For example, if `cache_file_name` is `"processed.arrow"`, then for `rank = 1` and `num_proc = 4`,
+ the resulting file would be `"processed_00001_of_00004.arrow"` for the default suffix (default
+ `_{rank:05d}_of_{num_proc:05d}`).
+ new_fingerprint (`str`, *optional*):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+ desc (`str`, *optional*, defaults to `None`):
+ Meaningful description to be displayed alongside with the progress bar while filtering examples.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.filter(lambda x: x["label"] == 1)
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 533
+ })
+ ```
+ """
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.filter` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it.`"
+ )
+
+ if function is None:
+ function = lambda x: True # noqa: E731
+
+ if len(self) == 0:
+ return self
+
+ indices = self.map(
+ function=partial(
+ get_indices_from_mask_function,
+ function,
+ batched,
+ with_indices,
+ with_rank,
+ input_columns,
+ self._indices,
+ ),
+ with_indices=True,
+ with_rank=True,
+ features=Features({"indices": Value("uint64")}),
+ batched=True,
+ batch_size=batch_size,
+ remove_columns=self.column_names,
+ keep_in_memory=keep_in_memory,
+ load_from_cache_file=load_from_cache_file,
+ cache_file_name=cache_file_name,
+ writer_batch_size=writer_batch_size,
+ fn_kwargs=fn_kwargs,
+ num_proc=num_proc,
+ suffix_template=suffix_template,
+ new_fingerprint=new_fingerprint,
+ input_columns=input_columns,
+ desc=desc or "Filter",
+ )
+ new_dataset = copy.deepcopy(self)
+ new_dataset._indices = indices.data
+ new_dataset._fingerprint = new_fingerprint
+ return new_dataset
+
+ @transmit_format
+ @fingerprint_transform(inplace=False, ignore_kwargs=["cache_file_name"])
+ def flatten_indices(
+ self,
+ keep_in_memory: bool = False,
+ cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ features: Optional[Features] = None,
+ disable_nullable: bool = False,
+ num_proc: Optional[int] = None,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create and cache a new Dataset by flattening the indices mapping.
+
+ Args:
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the dataset in memory instead of writing it to a cache file.
+ cache_file_name (`str`, *optional*, default `None`):
+ Provide the name of a path for the cache file. It is used to store the
+ results of the computation instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ features (`Optional[datasets.Features]`, defaults to `None`):
+ Use a specific [`Features`] to store the cache file
+ instead of the automatically generated one.
+ disable_nullable (`bool`, defaults to `False`):
+ Allow null values in the table.
+ num_proc (`int`, optional, default `None`):
+ Max number of processes when generating cache. Already cached shards are loaded sequentially
+ new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+ """
+
+ return self.map(
+ batched=True, # for speed
+ keep_in_memory=keep_in_memory,
+ cache_file_name=cache_file_name,
+ writer_batch_size=writer_batch_size,
+ features=features,
+ disable_nullable=disable_nullable,
+ new_fingerprint=new_fingerprint,
+ desc="Flattening the indices",
+ num_proc=num_proc,
+ )
+
+ def _new_dataset_with_indices(
+ self,
+ indices_cache_file_name: Optional[str] = None,
+ indices_buffer: Optional[pa.Buffer] = None,
+ fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Return a new Dataset obtained by adding indices (provided in indices_cache_file_name or in a buffer) to the
+ current Dataset.
+ """
+
+ if indices_cache_file_name is None and indices_buffer is None:
+ raise ValueError("At least one of indices_cache_file_name or indices_buffer must be provided.")
+
+ if fingerprint is None:
+ raise ValueError("please specify a fingerprint for the dataset with indices")
+
+ if indices_cache_file_name is not None:
+ indices_table = MemoryMappedTable.from_file(indices_cache_file_name)
+ else:
+ indices_table = InMemoryTable.from_buffer(indices_buffer)
+
+ # Return new Dataset object
+ # don't forget to copy the objects
+ return Dataset(
+ self._data,
+ info=self.info.copy(),
+ split=self.split,
+ indices_table=indices_table,
+ fingerprint=fingerprint,
+ )
+
+ @transmit_format
+ @fingerprint_transform(inplace=False, ignore_kwargs=["indices_cache_file_name"])
+ def select(
+ self,
+ indices: Iterable,
+ keep_in_memory: bool = False,
+ indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create a new dataset with rows selected following the list/array of indices.
+
+ Args:
+ indices (`range`, `list`, `iterable`, `ndarray` or `Series`):
+ Range, list or 1D-array of integer indices for indexing.
+ If the indices correspond to a contiguous range, the Arrow table is simply sliced.
+ However passing a list of indices that are not contiguous creates indices mapping, which is much less efficient,
+ but still faster than recreating an Arrow table made of the requested rows.
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the indices mapping in memory instead of writing it to a cache file.
+ indices_cache_file_name (`str`, *optional*, defaults to `None`):
+ Provide the name of a path for the cache file. It is used to store the
+ indices mapping instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds.select(range(4))
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 4
+ })
+ ```
+ """
+ if keep_in_memory and indices_cache_file_name is not None:
+ raise ValueError("Please use either `keep_in_memory` or `indices_cache_file_name` but not both.")
+
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.select` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return self
+
+ # If indices is a PyArrow array, we convert to NumPy
+ if isinstance(indices, (pa.Array, pa.ChunkedArray)):
+ indices = indices.to_numpy().astype(np.int64)
+
+ # Convert generator objects to lists
+ if isinstance(indices, Iterator):
+ indices = list(indices)
+
+ # If the indices are contiguous, simply slice the arrow table
+ if isinstance(indices, range):
+ if _is_range_contiguous(indices) and indices.start >= 0:
+ start, length = indices.start, indices.stop - indices.start
+ return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
+ else:
+ try:
+ start = next(iter(indices))
+ except StopIteration:
+ # if `indices` is an empty iterable, we return an empty dataset
+ return self._select_contiguous(0, 0, new_fingerprint=new_fingerprint)
+ if start >= 0:
+ counter_from_start = itertools.count(start=start)
+ if all(i == j for i, j in zip(indices, counter_from_start)):
+ length = next(counter_from_start) - start
+ return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
+
+ # If not contiguous, we need to create a new indices mapping
+ return self._select_with_indices_mapping(
+ indices,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=indices_cache_file_name,
+ writer_batch_size=writer_batch_size,
+ new_fingerprint=new_fingerprint,
+ )
+
+ @transmit_format
+ @fingerprint_transform(inplace=False)
+ def _select_contiguous(
+ self,
+ start: int,
+ length: int,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create a new dataset with rows from a contiguous slice of data.
+ The slice is defined by that start index and its length.
+
+ Args:
+ start (`int`): start index.
+ length (`int`): length of the slice to select.
+ new_fingerprint (`str`, optional, default `None`): the new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds._select_contiguous(0, 4)
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 4
+ })
+ ```
+ """
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.select` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return self
+
+ _check_valid_indices_value(start, len(self))
+ _check_valid_indices_value(start + length - 1, len(self))
+ if self._indices is None or length == 0:
+ return Dataset(
+ self.data.slice(start, length),
+ info=self.info.copy(),
+ split=self.split,
+ fingerprint=new_fingerprint,
+ )
+ else:
+ return Dataset(
+ self.data,
+ info=self.info.copy(),
+ split=self.split,
+ indices_table=self._indices.slice(start, length),
+ fingerprint=new_fingerprint,
+ )
+
+ @transmit_format
+ @fingerprint_transform(inplace=False, ignore_kwargs=["indices_cache_file_name"])
+ def _select_with_indices_mapping(
+ self,
+ indices: Iterable,
+ keep_in_memory: bool = False,
+ indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create a new dataset with rows selected following the list/array of indices.
+ The new dataset is made by creating a new indices mapping on top of the main arrow table.
+
+ Args:
+ indices (sequence, iterable, range, ndarray or Series): List or 1D-array of integer indices for indexing.
+ keep_in_memory (`bool`, default `False`): Keep the indices mapping in memory instead of writing it to a cache file.
+ indices_cache_file_name (`str`, optional, default `None`): Provide the name of a path for the cache file. It is used to store the
+ indices mapping instead of the automatically generated cache file name.
+ writer_batch_size (`int`, default `1000`): Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `.map()`.
+ new_fingerprint (`str`, optional, default `None`): the new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds._select_with_indices_mapping(range(4))
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 4
+ })
+ ```
+ """
+ if keep_in_memory and indices_cache_file_name is not None:
+ raise ValueError("Please use either `keep_in_memory` or `indices_cache_file_name` but not both.")
+
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.select` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return self
+
+ # Prepare the writer for our indices arrow table
+ if keep_in_memory or indices_cache_file_name is None:
+ buf_writer = pa.BufferOutputStream()
+ tmp_file = None
+ writer = ArrowWriter(
+ stream=buf_writer, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
+ )
+ else:
+ buf_writer = None
+ logger.info(f"Caching indices mapping at {indices_cache_file_name}")
+ tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
+ writer = ArrowWriter(
+ path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
+ )
+
+ indices = indices if isinstance(indices, list) else list(indices)
+
+ size = len(self)
+ if indices:
+ _check_valid_indices_value(int(max(indices)), size=size)
+ _check_valid_indices_value(int(min(indices)), size=size)
+ else:
+ return self._select_contiguous(0, 0, new_fingerprint=new_fingerprint)
+
+ indices_array = pa.array(indices, type=pa.uint64())
+ # Check if we need to convert indices
+ if self._indices is not None:
+ indices_array = self._indices.column(0).take(indices_array)
+
+ indices_table = pa.Table.from_arrays([indices_array], names=["indices"])
+
+ with writer:
+ try:
+ writer.write_table(indices_table)
+ writer.finalize() # close_stream=bool(buf_writer is None)) We only close if we are writing in a file
+ except (Exception, KeyboardInterrupt):
+ if tmp_file is not None:
+ tmp_file.close()
+ if os.path.exists(tmp_file.name):
+ os.remove(tmp_file.name)
+ raise
+
+ if tmp_file is not None:
+ tmp_file.close()
+ shutil.move(tmp_file.name, indices_cache_file_name)
+ umask = os.umask(0o666)
+ os.umask(umask)
+ os.chmod(indices_cache_file_name, 0o666 & ~umask)
+
+ # Return new Dataset object
+ if buf_writer is None:
+ return self._new_dataset_with_indices(
+ indices_cache_file_name=indices_cache_file_name, fingerprint=new_fingerprint
+ )
+ else:
+ return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
+
+ def skip(self, n: int) -> "Dataset":
+ """
+ Create a new [`Dataset`] that skips the first `n` elements.
+
+ Args:
+ n (`int`):
+ Number of elements to skip.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="train")
+ >>> list(ds.take(3))
+ [{'label': 1,
+ 'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'},
+ {'label': 1,
+ 'text': 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'},
+ {'label': 1, 'text': 'effective but too-tepid biopic'}]
+ >>> ds = ds.skip(1)
+ >>> list(ds.take(3))
+ [{'label': 1,
+ 'text': 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'},
+ {'label': 1, 'text': 'effective but too-tepid biopic'},
+ {'label': 1,
+ 'text': 'if you sometimes like to go to the movies to have fun , wasabi is a good place to start .'}]
+ ```
+ """
+ return self.select(range(n, len(self)))
+
+ def take(self, n: int) -> "Dataset":
+ """
+ Create a new [`Dataset`] with only the first `n` elements.
+
+ Args:
+ n (`int`):
+ Number of elements to take.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="train")
+ >>> small_ds = ds.take(2)
+ >>> list(small_ds)
+ [{'label': 1,
+ 'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'},
+ {'label': 1,
+ 'text': 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'}]
+ ```
+ """
+ return self.select(range(n))
+
+ @transmit_format
+ @fingerprint_transform(inplace=False, ignore_kwargs=["load_from_cache_file", "indices_cache_file_name"])
+ def sort(
+ self,
+ column_names: Union[str, Sequence_[str]],
+ reverse: Union[bool, Sequence_[bool]] = False,
+ kind="deprecated",
+ null_placement: str = "at_end",
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create a new dataset sorted according to a single or multiple columns.
+
+ Args:
+ column_names (`Union[str, Sequence[str]]`):
+ Column name(s) to sort by.
+ reverse (`Union[bool, Sequence[bool]]`, defaults to `False`):
+ If `True`, sort by descending order rather than ascending. If a single bool is provided,
+ the value is applied to the sorting of all column names. Otherwise a list of bools with the
+ same length and order as column_names must be provided.
+ kind (`str`, *optional*):
+ Pandas algorithm for sorting selected in `{quicksort, mergesort, heapsort, stable}`,
+ The default is `quicksort`. Note that both `stable` and `mergesort` use `timsort` under the covers and, in general,
+ the actual implementation will vary with data type. The `mergesort` option is retained for backwards compatibility.
+
+
+ `kind` was deprecated in version 2.10.0 and will be removed in 3.0.0.
+
+
+ null_placement (`str`, defaults to `at_end`):
+ Put `None` values at the beginning if `at_start` or `first` or at the end if `at_end` or `last`
+
+
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the sorted indices in memory instead of writing it to a cache file.
+ load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
+ If a cache file storing the sorted indices
+ can be identified, use it instead of recomputing.
+ indices_cache_file_name (`str`, *optional*, defaults to `None`):
+ Provide the name of a path for the cache file. It is used to store the
+ sorted indices instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ Higher value gives smaller cache files, lower value consume less temporary memory.
+ new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset('rotten_tomatoes', split='validation')
+ >>> ds['label'][:10]
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+ >>> sorted_ds = ds.sort('label')
+ >>> sorted_ds['label'][:10]
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+ >>> another_sorted_ds = ds.sort(['label', 'text'], reverse=[True, False])
+ >>> another_sorted_ds['label'][:10]
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+ ```
+ """
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.sort` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return self
+
+ # Deprecation warning
+ if kind != "deprecated":
+ warnings.warn(
+ "'kind' was deprecated in version 2.10.0 and will be removed in 3.0.0.",
+ category=FutureWarning,
+ )
+
+ # Check proper format of and for duplicates in column_names
+ if isinstance(column_names, str):
+ column_names = [column_names]
+
+ # Check proper format and length of reverse
+ if not isinstance(reverse, bool):
+ if len(reverse) != len(column_names):
+ raise ValueError(
+ "Parameter 'reverse' should be either a boolean or a list of booleans with the same length as 'column_names'."
+ )
+ else:
+ reverse = [reverse] * len(column_names)
+
+ # Check whether column name(s) exist in dataset
+ for column in column_names:
+ if not isinstance(column, str) or column not in self._data.column_names:
+ raise ValueError(
+ f"Column '{column}' not found in the dataset. Please provide a column selected in: {self._data.column_names}"
+ )
+
+ # Change null_placement to conform to pyarrow's sort_indices() while ensuring backwards compatability
+ if null_placement not in ["at_start", "at_end"]:
+ if null_placement == "first":
+ null_placement = "at_start"
+ elif null_placement == "last":
+ null_placement = "at_end"
+ else:
+ raise ValueError(
+ f"null_placement '{null_placement}' is an invalid parameter value. Must be either 'last', 'at_end', 'first' or 'at_start'."
+ )
+
+ load_from_cache_file = load_from_cache_file if load_from_cache_file is not None else is_caching_enabled()
+
+ # Check if we've already cached this computation (indexed by a hash)
+ if self.cache_files:
+ if indices_cache_file_name is None:
+ # we create a unique hash from the function, current dataset file and the mapping args
+ indices_cache_file_name = self._get_cache_file_path(new_fingerprint)
+ if os.path.exists(indices_cache_file_name) and load_from_cache_file:
+ logger.info(f"Loading cached sorted indices for dataset at {indices_cache_file_name}")
+ return self._new_dataset_with_indices(
+ fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
+ )
+
+ sort_table = query_table(
+ table=self._data,
+ key=slice(0, len(self)),
+ indices=self._indices,
+ )
+
+ sort_keys = [
+ (col, "ascending" if not col_reverse else "descending") for col, col_reverse in zip(column_names, reverse)
+ ]
+
+ indices = pc.sort_indices(sort_table, sort_keys=sort_keys, null_placement=null_placement)
+
+ return self.select(
+ indices=indices,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=indices_cache_file_name,
+ writer_batch_size=writer_batch_size,
+ new_fingerprint=new_fingerprint,
+ )
+
+ @transmit_format
+ @fingerprint_transform(
+ inplace=False, randomized_function=True, ignore_kwargs=["load_from_cache_file", "indices_cache_file_name"]
+ )
+ def shuffle(
+ self,
+ seed: Optional[int] = None,
+ generator: Optional[np.random.Generator] = None,
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ new_fingerprint: Optional[str] = None,
+ ) -> "Dataset":
+ """Create a new Dataset where the rows are shuffled.
+
+ Currently shuffling uses numpy random generators.
+ You can either supply a NumPy BitGenerator to use, or a seed to initiate NumPy's default random generator (PCG64).
+
+ Shuffling takes the list of indices `[0:len(my_dataset)]` and shuffles it to create an indices mapping.
+ However as soon as your [`Dataset`] has an indices mapping, the speed can become 10x slower.
+ This is because there is an extra step to get the row index to read using the indices mapping, and most importantly, you aren't reading contiguous chunks of data anymore.
+ To restore the speed, you'd need to rewrite the entire dataset on your disk again using [`Dataset.flatten_indices`], which removes the indices mapping.
+ This may take a lot of time depending of the size of your dataset though:
+
+ ```python
+ my_dataset[0] # fast
+ my_dataset = my_dataset.shuffle(seed=42)
+ my_dataset[0] # up to 10x slower
+ my_dataset = my_dataset.flatten_indices() # rewrite the shuffled dataset on disk as contiguous chunks of data
+ my_dataset[0] # fast again
+ ```
+
+ In this case, we recommend switching to an [`IterableDataset`] and leveraging its fast approximate shuffling method [`IterableDataset.shuffle`].
+ It only shuffles the shards order and adds a shuffle buffer to your dataset, which keeps the speed of your dataset optimal:
+
+ ```python
+ my_iterable_dataset = my_dataset.to_iterable_dataset(num_shards=128)
+ for example in enumerate(my_iterable_dataset): # fast
+ pass
+
+ shuffled_iterable_dataset = my_iterable_dataset.shuffle(seed=42, buffer_size=100)
+
+ for example in enumerate(shuffled_iterable_dataset): # as fast as before
+ pass
+ ```
+
+ Args:
+ seed (`int`, *optional*):
+ A seed to initialize the default BitGenerator if `generator=None`.
+ If `None`, then fresh, unpredictable entropy will be pulled from the OS.
+ If an `int` or `array_like[ints]` is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state.
+ generator (`numpy.random.Generator`, *optional*):
+ Numpy random Generator to use to compute the permutation of the dataset rows.
+ If `generator=None` (default), uses `np.random.default_rng` (the default BitGenerator (PCG64) of NumPy).
+ keep_in_memory (`bool`, default `False`):
+ Keep the shuffled indices in memory instead of writing it to a cache file.
+ load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
+ If a cache file storing the shuffled indices
+ can be identified, use it instead of recomputing.
+ indices_cache_file_name (`str`, *optional*):
+ Provide the name of a path for the cache file. It is used to store the
+ shuffled indices instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the dataset after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds['label'][:10]
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+ # set a seed
+ >>> shuffled_ds = ds.shuffle(seed=42)
+ >>> shuffled_ds['label'][:10]
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0]
+ ```
+ """
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.shuffle` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return self
+
+ if keep_in_memory and indices_cache_file_name is not None:
+ raise ValueError("Please use either `keep_in_memory` or `indices_cache_file_name` but not both.")
+
+ if seed is not None and generator is not None:
+ raise ValueError("Both `seed` and `generator` were provided. Please specify just one of them.")
+
+ if generator is not None and not isinstance(generator, np.random.Generator):
+ raise ValueError("The provided generator must be an instance of numpy.random.Generator")
+
+ load_from_cache_file = load_from_cache_file if load_from_cache_file is not None else is_caching_enabled()
+
+ if generator is None:
+ if seed is None:
+ _, seed, pos, *_ = np.random.get_state()
+ seed = seed[pos] if pos < 624 else seed[0]
+ _ = np.random.random() # do 1 step of rng
+ generator = np.random.default_rng(seed)
+
+ # Check if we've already cached this computation (indexed by a hash)
+ if self.cache_files:
+ if indices_cache_file_name is None:
+ # we create a unique hash from the function, current dataset file and the mapping args
+ indices_cache_file_name = self._get_cache_file_path(new_fingerprint)
+ if os.path.exists(indices_cache_file_name) and load_from_cache_file:
+ logger.info(f"Loading cached shuffled indices for dataset at {indices_cache_file_name}")
+ return self._new_dataset_with_indices(
+ fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
+ )
+
+ permutation = generator.permutation(len(self))
+
+ return self.select(
+ indices=permutation,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
+ writer_batch_size=writer_batch_size,
+ new_fingerprint=new_fingerprint,
+ )
+
+ @transmit_format
+ @fingerprint_transform(
+ inplace=False,
+ randomized_function=True,
+ fingerprint_names=["train_new_fingerprint", "test_new_fingerprint"],
+ ignore_kwargs=["load_from_cache_file", "train_indices_cache_file_name", "test_indices_cache_file_name"],
+ )
+ def train_test_split(
+ self,
+ test_size: Union[float, int, None] = None,
+ train_size: Union[float, int, None] = None,
+ shuffle: bool = True,
+ stratify_by_column: Optional[str] = None,
+ seed: Optional[int] = None,
+ generator: Optional[np.random.Generator] = None,
+ keep_in_memory: bool = False,
+ load_from_cache_file: Optional[bool] = None,
+ train_indices_cache_file_name: Optional[str] = None,
+ test_indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ train_new_fingerprint: Optional[str] = None,
+ test_new_fingerprint: Optional[str] = None,
+ ) -> "DatasetDict":
+ """Return a dictionary ([`datasets.DatasetDict`]) with two random train and test subsets (`train` and `test` `Dataset` splits).
+ Splits are created from the dataset according to `test_size`, `train_size` and `shuffle`.
+
+ This method is similar to scikit-learn `train_test_split`.
+
+ Args:
+ test_size (`numpy.random.Generator`, *optional*):
+ Size of the test split
+ If `float`, should be between `0.0` and `1.0` and represent the proportion of the dataset to include in the test split.
+ If `int`, represents the absolute number of test samples.
+ If `None`, the value is set to the complement of the train size.
+ If `train_size` is also `None`, it will be set to `0.25`.
+ train_size (`numpy.random.Generator`, *optional*):
+ Size of the train split
+ If `float`, should be between `0.0` and `1.0` and represent the proportion of the dataset to include in the train split.
+ If `int`, represents the absolute number of train samples.
+ If `None`, the value is automatically set to the complement of the test size.
+ shuffle (`bool`, *optional*, defaults to `True`):
+ Whether or not to shuffle the data before splitting.
+ stratify_by_column (`str`, *optional*, defaults to `None`):
+ The column name of labels to be used to perform stratified split of data.
+ seed (`int`, *optional*):
+ A seed to initialize the default BitGenerator if `generator=None`.
+ If `None`, then fresh, unpredictable entropy will be pulled from the OS.
+ If an `int` or `array_like[ints]` is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state.
+ generator (`numpy.random.Generator`, *optional*):
+ Numpy random Generator to use to compute the permutation of the dataset rows.
+ If `generator=None` (default), uses `np.random.default_rng` (the default BitGenerator (PCG64) of NumPy).
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the splits indices in memory instead of writing it to a cache file.
+ load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
+ If a cache file storing the splits indices
+ can be identified, use it instead of recomputing.
+ train_cache_file_name (`str`, *optional*):
+ Provide the name of a path for the cache file. It is used to store the
+ train split indices instead of the automatically generated cache file name.
+ test_cache_file_name (`str`, *optional*):
+ Provide the name of a path for the cache file. It is used to store the
+ test split indices instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+ train_new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the train set after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+ test_new_fingerprint (`str`, *optional*, defaults to `None`):
+ The new fingerprint of the test set after transform.
+ If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds = ds.train_test_split(test_size=0.2, shuffle=True)
+ DatasetDict({
+ train: Dataset({
+ features: ['text', 'label'],
+ num_rows: 852
+ })
+ test: Dataset({
+ features: ['text', 'label'],
+ num_rows: 214
+ })
+ })
+
+ # set a seed
+ >>> ds = ds.train_test_split(test_size=0.2, seed=42)
+
+ # stratified split
+ >>> ds = load_dataset("imdb",split="train")
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 25000
+ })
+ >>> ds = ds.train_test_split(test_size=0.2, stratify_by_column="label")
+ DatasetDict({
+ train: Dataset({
+ features: ['text', 'label'],
+ num_rows: 20000
+ })
+ test: Dataset({
+ features: ['text', 'label'],
+ num_rows: 5000
+ })
+ })
+ ```
+ """
+ from .dataset_dict import DatasetDict # import here because of circular dependency
+
+ if len(self.list_indexes()) > 0:
+ raise DatasetTransformationNotAllowedError(
+ "Using `.train_test_split` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
+ )
+ # If the array is empty we do nothing
+ if len(self) == 0:
+ return DatasetDict({"train": self, "test": self})
+
+ if test_size is None and train_size is None:
+ test_size = 0.25
+
+ # Safety checks similar to scikit-learn's ones.
+ # (adapted from https://github.com/scikit-learn/scikit-learn/blob/fd237278e895b42abe8d8d09105cbb82dc2cbba7/sklearn/model_selection/_split.py#L1750)
+ n_samples = len(self)
+ if (
+ isinstance(test_size, int)
+ and (test_size >= n_samples or test_size <= 0)
+ or isinstance(test_size, float)
+ and (test_size <= 0 or test_size >= 1)
+ ):
+ raise ValueError(
+ f"test_size={test_size} should be either positive and smaller "
+ f"than the number of samples {n_samples} or a float in the (0, 1) range"
+ )
+
+ if (
+ isinstance(train_size, int)
+ and (train_size >= n_samples or train_size <= 0)
+ or isinstance(train_size, float)
+ and (train_size <= 0 or train_size >= 1)
+ ):
+ raise ValueError(
+ f"train_size={train_size} should be either positive and smaller "
+ f"than the number of samples {n_samples} or a float in the (0, 1) range"
+ )
+
+ if train_size is not None and not isinstance(train_size, (int, float)):
+ raise ValueError(f"Invalid value for train_size: {train_size} of type {type(train_size)}")
+ if test_size is not None and not isinstance(test_size, (int, float)):
+ raise ValueError(f"Invalid value for test_size: {test_size} of type {type(test_size)}")
+
+ if isinstance(train_size, float) and isinstance(test_size, float) and train_size + test_size > 1:
+ raise ValueError(
+ f"The sum of test_size and train_size = {train_size + test_size}, should be in the (0, 1)"
+ " range. Reduce test_size and/or train_size."
+ )
+
+ if isinstance(test_size, float):
+ n_test = ceil(test_size * n_samples)
+ elif isinstance(test_size, int):
+ n_test = float(test_size)
+
+ if isinstance(train_size, float):
+ n_train = floor(train_size * n_samples)
+ elif isinstance(train_size, int):
+ n_train = float(train_size)
+
+ if train_size is None:
+ n_train = n_samples - n_test
+ elif test_size is None:
+ n_test = n_samples - n_train
+
+ if n_train + n_test > n_samples:
+ raise ValueError(
+ f"The sum of train_size and test_size = {n_train + n_test}, "
+ "should be smaller than the number of "
+ f"samples {n_samples}. Reduce test_size and/or "
+ "train_size."
+ )
+
+ n_train, n_test = int(n_train), int(n_test)
+
+ if n_train == 0:
+ raise ValueError(
+ f"With n_samples={n_samples}, test_size={test_size} and train_size={train_size}, the "
+ "resulting train set will be empty. Adjust any of the "
+ "aforementioned parameters."
+ )
+
+ load_from_cache_file = load_from_cache_file if load_from_cache_file is not None else is_caching_enabled()
+
+ if generator is None and shuffle is True:
+ if seed is None:
+ _, seed, pos, *_ = np.random.get_state()
+ seed = seed[pos] if pos < 624 else seed[0]
+ _ = np.random.random() # do 1 step of rng
+ generator = np.random.default_rng(seed)
+
+ # Check if we've already cached this computation (indexed by a hash)
+ if self.cache_files:
+ if train_indices_cache_file_name is None or test_indices_cache_file_name is None:
+ # we create a unique hash from the function, current dataset file and the mapping args
+
+ if train_indices_cache_file_name is None:
+ train_indices_cache_file_name = self._get_cache_file_path(train_new_fingerprint)
+ if test_indices_cache_file_name is None:
+ test_indices_cache_file_name = self._get_cache_file_path(test_new_fingerprint)
+ if (
+ os.path.exists(train_indices_cache_file_name)
+ and os.path.exists(test_indices_cache_file_name)
+ and load_from_cache_file
+ ):
+ logger.info(
+ f"Loading cached split indices for dataset at {train_indices_cache_file_name} and {test_indices_cache_file_name}"
+ )
+ return DatasetDict(
+ {
+ "train": self._new_dataset_with_indices(
+ fingerprint=train_new_fingerprint, indices_cache_file_name=train_indices_cache_file_name
+ ),
+ "test": self._new_dataset_with_indices(
+ fingerprint=test_new_fingerprint, indices_cache_file_name=test_indices_cache_file_name
+ ),
+ }
+ )
+ if not shuffle:
+ if stratify_by_column is not None:
+ raise ValueError("Stratified train/test split is not implemented for `shuffle=False`")
+ train_indices = np.arange(n_train)
+ test_indices = np.arange(n_train, n_train + n_test)
+ else:
+ # stratified partition
+ if stratify_by_column is not None:
+ if stratify_by_column not in self._info.features.keys():
+ raise ValueError(f"Key {stratify_by_column} not found in {self._info.features.keys()}")
+ if not isinstance(self._info.features[stratify_by_column], ClassLabel):
+ raise ValueError(
+ f"Stratifying by column is only supported for {ClassLabel.__name__} column, and column {stratify_by_column} is {type(self._info.features[stratify_by_column]).__name__}."
+ )
+ try:
+ train_indices, test_indices = next(
+ stratified_shuffle_split_generate_indices(
+ self.with_format("numpy")[stratify_by_column], n_train, n_test, rng=generator
+ )
+ )
+ except Exception as error:
+ if str(error) == "Minimum class count error":
+ raise ValueError(
+ f"The least populated class in {stratify_by_column} column has only 1"
+ " member, which is too few. The minimum"
+ " number of groups for any class cannot"
+ " be less than 2."
+ )
+ else:
+ raise error
+
+ # random partition
+ else:
+ permutation = generator.permutation(len(self))
+ test_indices = permutation[:n_test]
+ train_indices = permutation[n_test : (n_test + n_train)]
+
+ train_split = self.select(
+ indices=train_indices,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=train_indices_cache_file_name,
+ writer_batch_size=writer_batch_size,
+ new_fingerprint=train_new_fingerprint,
+ )
+ test_split = self.select(
+ indices=test_indices,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=test_indices_cache_file_name,
+ writer_batch_size=writer_batch_size,
+ new_fingerprint=test_new_fingerprint,
+ )
+
+ return DatasetDict({"train": train_split, "test": test_split})
+
+ def shard(
+ self,
+ num_shards: int,
+ index: int,
+ contiguous: bool = False,
+ keep_in_memory: bool = False,
+ indices_cache_file_name: Optional[str] = None,
+ writer_batch_size: Optional[int] = 1000,
+ ) -> "Dataset":
+ """Return the `index`-nth shard from dataset split into `num_shards` pieces.
+
+ This shards deterministically. `dset.shard(n, i)` will contain all elements of dset whose
+ index mod `n = i`.
+
+ `dset.shard(n, i, contiguous=True)` will instead split dset into contiguous chunks,
+ so it can be easily concatenated back together after processing. If `n % i == l`, then the
+ first `l` shards will have length `(n // i) + 1`, and the remaining shards will have length `(n // i)`.
+ `datasets.concatenate([dset.shard(n, i, contiguous=True) for i in range(n)])` will return
+ a dataset with the same order as the original.
+
+ Be sure to shard before using any randomizing operator (such as `shuffle`).
+ It is best if the shard operator is used early in the dataset pipeline.
+
+
+ Args:
+ num_shards (`int`):
+ How many shards to split the dataset into.
+ index (`int`):
+ Which shard to select and return.
+ contiguous: (`bool`, defaults to `False`):
+ Whether to select contiguous blocks of indices for shards.
+ keep_in_memory (`bool`, defaults to `False`):
+ Keep the dataset in memory instead of writing it to a cache file.
+ indices_cache_file_name (`str`, *optional*):
+ Provide the name of a path for the cache file. It is used to store the
+ indices of each shard instead of the automatically generated cache file name.
+ writer_batch_size (`int`, defaults to `1000`):
+ Number of rows per write operation for the cache file writer.
+ This value is a good trade-off between memory usage during the processing, and processing speed.
+ Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> ds
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 1066
+ })
+ >>> ds.shard(num_shards=2, index=0)
+ Dataset({
+ features: ['text', 'label'],
+ num_rows: 533
+ })
+ ```
+ """
+ if not 0 <= index < num_shards:
+ raise ValueError("index should be in [0, num_shards-1]")
+ if contiguous:
+ div = len(self) // num_shards
+ mod = len(self) % num_shards
+ start = div * index + min(index, mod)
+ end = start + div + (1 if index < mod else 0)
+ indices = range(start, end)
+ else:
+ indices = np.arange(index, len(self), num_shards)
+
+ return self.select(
+ indices=indices,
+ keep_in_memory=keep_in_memory,
+ indices_cache_file_name=indices_cache_file_name,
+ writer_batch_size=writer_batch_size,
+ )
+
+ @deprecated()
+ def export(
+ self,
+ filename: str,
+ format: str = "tfrecord",
+ ):
+ """Writes the Arrow dataset to a TFRecord file.
+
+ The dataset must already be in tensorflow format. The records will be written with
+ keys from `dataset._format_columns`.
+
+ Args:
+ filename (`str`): The filename, including the `.tfrecord` extension, to write to.
+ format (`str`, optional, default `"tfrecord"`): The type of output file. Currently this is a no-op, as
+ TFRecords are the only option. This enables a more flexible function signature later.
+ """
+ try:
+ import tensorflow as tf # noqa: F401
+ except ImportError:
+ logger.error("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
+
+ # From https://www.tensorflow.org/tutorials/load_data/tfrecord
+ def _bytes_feature(values):
+ """Returns a bytes_list from a list of string / byte."""
+ return tf.train.Feature(bytes_list=tf.train.BytesList(value=values))
+
+ def _float_feature(values):
+ """Returns a float_list from a list of float / double."""
+ return tf.train.Feature(float_list=tf.train.FloatList(value=values))
+
+ def _int64_feature(values):
+ """Returns an int64_list from a list of bool / enum / int / uint."""
+ return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
+
+ def _feature(values: Union[float, int, str, np.ndarray, list]) -> "tf.train.Feature":
+ """Typechecks `values` and returns the corresponding tf.train.Feature."""
+ if isinstance(values, list):
+ if values and isinstance(values[0], str):
+ return _bytes_feature([v.encode() for v in values])
+ else:
+ raise ValueError(f"values={values} is empty or contains items that cannot be serialized")
+ elif isinstance(values, np.ndarray):
+ if values.dtype == np.dtype(float):
+ return _float_feature(values)
+ elif values.dtype == np.int64:
+ return _int64_feature(values)
+ elif values.dtype == np.dtype(str) or (
+ values.dtype == np.dtype(object) and len(values) > 0 and isinstance(values[0], str)
+ ):
+ return _bytes_feature([v.encode() for v in values])
+ else:
+ raise ValueError(
+ f"values={values} is empty or is an np.ndarray with items of dtype {values[0].dtype}, which cannot be serialized"
+ )
+ elif hasattr(values, "dtype"):
+ if np.issubdtype(values.dtype, np.floating):
+ return _float_feature([values.item()])
+ elif np.issubdtype(values.dtype, np.integer):
+ return _int64_feature([values.item()])
+ elif np.issubdtype(values.dtype, str):
+ return _bytes_feature([values.item().encode()])
+ else:
+ raise ValueError(f"values={values} has dtype {values.dtype}, which cannot be serialized")
+ else:
+ raise ValueError(f"values={values} are not numpy objects or strings, and so cannot be serialized")
+
+ def serialize_example(ex):
+ feature = {key: _feature(value) for key, value in ex.items()}
+ example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
+ return example_proto.SerializeToString()
+
+ def tf_serialize_example(ex):
+ tf_string = tf.py_function(serialize_example, (ex,), tf.string)
+ return tf.reshape(tf_string, ())
+
+ def generator():
+ for ex in self:
+ yield serialize_example(ex)
+
+ if self._format_type != "numpy":
+ raise ValueError("Dataset format must be numpy before exporting")
+ if not filename.endswith(".tfrecord"):
+ raise ValueError("filename {filename} must end with .tfrecord")
+ tf_dataset = tf.data.Dataset.from_generator(generator, output_types=tf.string, output_shapes=())
+ writer = tf.data.experimental.TFRecordWriter(filename)
+ logger.info(f"Writing TFRecord to {filename}")
+ writer.write(tf_dataset)
+ logger.info(f"Finished writing TFRecord to {filename}")
+ self = None # delete the dataset reference used by tf_dataset
+
+ def to_csv(
+ self,
+ path_or_buf: Union[PathLike, BinaryIO],
+ batch_size: Optional[int] = None,
+ num_proc: Optional[int] = None,
+ storage_options: Optional[dict] = None,
+ **to_csv_kwargs,
+ ) -> int:
+ """Exports the dataset to csv
+
+ Args:
+ path_or_buf (`PathLike` or `FileOrBuffer`):
+ Either a path to a file (e.g. `file.csv`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.csv`),
+ or a BinaryIO, where the dataset will be saved to in the specified format.
+ batch_size (`int`, *optional*):
+ Size of the batch to load in memory and write at once.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+ num_proc (`int`, *optional*):
+ Number of processes for multiprocessing. By default it doesn't
+ use multiprocessing. `batch_size` in this case defaults to
+ `datasets.config.DEFAULT_MAX_BATCH_SIZE` but feel free to make it 5x or 10x of the default
+ value if you have sufficient compute power.
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the file-system backend, if any.
+
+
+ **to_csv_kwargs (additional keyword arguments):
+ Parameters to pass to pandas's [`pandas.DataFrame.to_csv`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html).
+
+
+
+ Now, `index` defaults to `False` if not specified.
+
+ If you would like to write the index, pass `index=True` and also set a name for the index column by
+ passing `index_label`.
+
+
+
+ Returns:
+ `int`: The number of characters or bytes written.
+
+ Example:
+
+ ```py
+ >>> ds.to_csv("path/to/dataset/directory")
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.csv import CsvDatasetWriter
+
+ return CsvDatasetWriter(
+ self,
+ path_or_buf,
+ batch_size=batch_size,
+ num_proc=num_proc,
+ storage_options=storage_options,
+ **to_csv_kwargs,
+ ).write()
+
+ def to_dict(self, batch_size: Optional[int] = None, batched="deprecated") -> Union[dict, Iterator[dict]]:
+ """Returns the dataset as a Python dict. Can also return a generator for large datasets.
+
+ Args:
+ batched (`bool`):
+ Set to `True` to return a generator that yields the dataset as batches
+ of `batch_size` rows. Defaults to `False` (returns the whole datasets once).
+
+
+
+ Use `.iter(batch_size=batch_size)` followed by `.to_dict()` on the individual batches instead.
+
+
+
+ batch_size (`int`, *optional*): The size (number of rows) of the batches if `batched` is `True`.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+
+ Returns:
+ `dict` or `Iterator[dict]`
+
+ Example:
+
+ ```py
+ >>> ds.to_dict()
+ ```
+ """
+ if batched != "deprecated":
+ warnings.warn(
+ "'batched' was deprecated in version 2.11.0 and will be removed in version 3.0.0. Use `.iter(batch_size=batch_size)` followed by `.to_dict()` on the individual batches instead.",
+ FutureWarning,
+ )
+ else:
+ batched = False
+
+ if not batched:
+ return query_table(
+ table=self._data,
+ key=slice(0, len(self)),
+ indices=self._indices,
+ ).to_pydict()
+ else:
+ batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
+ return (
+ query_table(
+ table=self._data,
+ key=slice(offset, offset + batch_size),
+ indices=self._indices,
+ ).to_pydict()
+ for offset in range(0, len(self), batch_size)
+ )
+
+ def to_list(self) -> list:
+ """Returns the dataset as a Python list.
+
+ Returns:
+ `list`
+
+ Example:
+
+ ```py
+ >>> ds.to_list()
+ ```
+ """
+ return query_table(
+ table=self._data,
+ key=slice(0, len(self)),
+ indices=self._indices,
+ ).to_pylist()
+
+ def to_json(
+ self,
+ path_or_buf: Union[PathLike, BinaryIO],
+ batch_size: Optional[int] = None,
+ num_proc: Optional[int] = None,
+ storage_options: Optional[dict] = None,
+ **to_json_kwargs,
+ ) -> int:
+ """Export the dataset to JSON Lines or JSON.
+
+ Args:
+ path_or_buf (`PathLike` or `FileOrBuffer`):
+ Either a path to a file (e.g. `file.json`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.json`),
+ or a BinaryIO, where the dataset will be saved to in the specified format.
+ batch_size (`int`, *optional*):
+ Size of the batch to load in memory and write at once.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+ num_proc (`int`, *optional*):
+ Number of processes for multiprocessing. By default it doesn't
+ use multiprocessing. `batch_size` in this case defaults to
+ `datasets.config.DEFAULT_MAX_BATCH_SIZE` but feel free to make it 5x or 10x of the default
+ value if you have sufficient compute power.
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the file-system backend, if any.
+
+
+ **to_json_kwargs (additional keyword arguments):
+ Parameters to pass to pandas's [`pandas.DataFrame.to_json`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html).
+
+
+
+ Now, `index` defaults to `False` if `orient` is `"split"` or `"table"`.
+
+ If you would like to write the index, pass `index=True`.
+
+
+
+ Returns:
+ `int`: The number of characters or bytes written.
+
+ Example:
+
+ ```py
+ >>> ds.to_json("path/to/dataset/directory")
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.json import JsonDatasetWriter
+
+ return JsonDatasetWriter(
+ self,
+ path_or_buf,
+ batch_size=batch_size,
+ num_proc=num_proc,
+ storage_options=storage_options,
+ **to_json_kwargs,
+ ).write()
+
+ def to_pandas(
+ self, batch_size: Optional[int] = None, batched: bool = False
+ ) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
+ """Returns the dataset as a `pandas.DataFrame`. Can also return a generator for large datasets.
+
+ Args:
+ batched (`bool`):
+ Set to `True` to return a generator that yields the dataset as batches
+ of `batch_size` rows. Defaults to `False` (returns the whole datasets once).
+ batch_size (`int`, *optional*):
+ The size (number of rows) of the batches if `batched` is `True`.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+
+ Returns:
+ `pandas.DataFrame` or `Iterator[pandas.DataFrame]`
+
+ Example:
+
+ ```py
+ >>> ds.to_pandas()
+ ```
+ """
+ if not batched:
+ return query_table(
+ table=self._data,
+ key=slice(0, len(self)),
+ indices=self._indices,
+ ).to_pandas(types_mapper=pandas_types_mapper)
+ else:
+ batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
+ return (
+ query_table(
+ table=self._data,
+ key=slice(offset, offset + batch_size),
+ indices=self._indices,
+ ).to_pandas(types_mapper=pandas_types_mapper)
+ for offset in range(0, len(self), batch_size)
+ )
+
+ def to_polars(
+ self,
+ batch_size: Optional[int] = None,
+ batched: bool = False,
+ schema_overrides: Optional[dict] = None,
+ rechunk: bool = True,
+ ) -> Union["pl.DataFrame", Iterator["pl.DataFrame"]]:
+ """Returns the dataset as a `polars.DataFrame`. Can also return a generator for large datasets.
+
+ Args:
+ batched (`bool`):
+ Set to `True` to return a generator that yields the dataset as batches
+ of `batch_size` rows. Defaults to `False` (returns the whole datasets once).
+ batch_size (`int`, *optional*):
+ The size (number of rows) of the batches if `batched` is `True`.
+ Defaults to `genomicsml.datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+ schema_overrides (`dict`, *optional*):
+ Support type specification or override of one or more columns; note that
+ any dtypes inferred from the schema param will be overridden.
+ rechunk (`bool`):
+ Make sure that all data is in contiguous memory. Defaults to `True`.
+ Returns:
+ `polars.DataFrame` or `Iterator[polars.DataFrame]`
+
+ Example:
+
+ ```py
+ >>> ds.to_polars()
+ ```
+ """
+ if config.POLARS_AVAILABLE:
+ import polars as pl
+
+ if not batched:
+ return pl.from_arrow(
+ query_table(
+ table=self._data,
+ key=slice(0, len(self)),
+ indices=self._indices if self._indices is not None else None,
+ ),
+ schema_overrides=schema_overrides,
+ rechunk=rechunk,
+ )
+ else:
+ batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
+ return (
+ pl.from_arrow(
+ query_table(
+ table=self._data,
+ key=slice(offset, offset + batch_size),
+ indices=self._indices if self._indices is not None else None,
+ ),
+ schema_overrides=schema_overrides,
+ rechunk=rechunk,
+ )
+ for offset in range(0, len(self), batch_size)
+ )
+ else:
+ raise ValueError("Polars needs to be installed to be able to return Polars dataframes.")
+
+ def to_parquet(
+ self,
+ path_or_buf: Union[PathLike, BinaryIO],
+ batch_size: Optional[int] = None,
+ storage_options: Optional[dict] = None,
+ **parquet_writer_kwargs,
+ ) -> int:
+ """Exports the dataset to parquet
+
+ Args:
+ path_or_buf (`PathLike` or `FileOrBuffer`):
+ Either a path to a file (e.g. `file.parquet`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.parquet`),
+ or a BinaryIO, where the dataset will be saved to in the specified format.
+ batch_size (`int`, *optional*):
+ Size of the batch to load in memory and write at once.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+ storage_options (`dict`, *optional*):
+ Key/value pairs to be passed on to the file-system backend, if any.
+
+
+ **parquet_writer_kwargs (additional keyword arguments):
+ Parameters to pass to PyArrow's `pyarrow.parquet.ParquetWriter`.
+
+ Returns:
+ `int`: The number of characters or bytes written.
+
+ Example:
+
+ ```py
+ >>> ds.to_parquet("path/to/dataset/directory")
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.parquet import ParquetDatasetWriter
+
+ return ParquetDatasetWriter(
+ self, path_or_buf, batch_size=batch_size, storage_options=storage_options, **parquet_writer_kwargs
+ ).write()
+
+ def to_sql(
+ self,
+ name: str,
+ con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
+ batch_size: Optional[int] = None,
+ **sql_writer_kwargs,
+ ) -> int:
+ """Exports the dataset to a SQL database.
+
+ Args:
+ name (`str`):
+ Name of SQL table.
+ con (`str` or `sqlite3.Connection` or `sqlalchemy.engine.Connection` or `sqlalchemy.engine.Connection`):
+ A [URI string](https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls) or a SQLite3/SQLAlchemy connection object used to write to a database.
+ batch_size (`int`, *optional*):
+ Size of the batch to load in memory and write at once.
+ Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`.
+ **sql_writer_kwargs (additional keyword arguments):
+ Parameters to pass to pandas's [`pandas.DataFrame.to_sql`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html).
+
+
+
+ Now, `index` defaults to `False` if not specified.
+
+ If you would like to write the index, pass `index=True` and also set a name for the index column by
+ passing `index_label`.
+
+
+
+ Returns:
+ `int`: The number of records written.
+
+ Example:
+
+ ```py
+ >>> # con provided as a connection URI string
+ >>> ds.to_sql("data", "sqlite:///my_own_db.sql")
+ >>> # con provided as a sqlite3 connection object
+ >>> import sqlite3
+ >>> con = sqlite3.connect("my_own_db.sql")
+ >>> with con:
+ ... ds.to_sql("data", con)
+ ```
+ """
+ # Dynamic import to avoid circular dependency
+ from .io.sql import SqlDatasetWriter
+
+ return SqlDatasetWriter(self, name, con, batch_size=batch_size, **sql_writer_kwargs).write()
+
+ def _estimate_nbytes(self) -> int:
+ dataset_nbytes = self.data.nbytes
+
+ # Find decodable columns, because if there are any, we need to
+ # adjust the dataset size computation (needed for sharding) to account for possible external files
+ decodable_columns = [
+ k for k, v in self._info.features.items() if require_decoding(v, ignore_decode_attribute=True)
+ ]
+
+ if decodable_columns:
+ # Approximate the space needed to store the bytes from the external files by analyzing the first 1000 examples
+ extra_nbytes = 0
+
+ def extra_nbytes_visitor(array, feature):
+ nonlocal extra_nbytes
+ if isinstance(feature, (Audio, Image)):
+ for x in array.to_pylist():
+ if x is not None and x["bytes"] is None and x["path"] is not None:
+ size = xgetsize(x["path"])
+ extra_nbytes += size
+ extra_nbytes -= array.field("path").nbytes
+
+ table = self.with_format("arrow")[:1000]
+ table_visitor(table, extra_nbytes_visitor)
+
+ extra_nbytes = extra_nbytes * len(self.data) / len(table)
+ dataset_nbytes = dataset_nbytes + extra_nbytes
+
+ if self._indices is not None:
+ dataset_nbytes = dataset_nbytes * len(self._indices) / len(self.data)
+ return dataset_nbytes
+
+ @staticmethod
+ def _generate_tables_from_shards(shards: List["Dataset"], batch_size: int):
+ for shard_idx, shard in enumerate(shards):
+ for pa_table in shard.with_format("arrow").iter(batch_size):
+ yield shard_idx, pa_table
+
+ @staticmethod
+ def _generate_tables_from_cache_file(filename: str):
+ for batch_idx, batch in enumerate(_memory_mapped_record_batch_reader_from_file(filename)):
+ yield batch_idx, pa.Table.from_batches([batch])
+
+ def to_iterable_dataset(self, num_shards: Optional[int] = 1) -> "IterableDataset":
+ """Get an [`datasets.IterableDataset`] from a map-style [`datasets.Dataset`].
+ This is equivalent to loading a dataset in streaming mode with [`datasets.load_dataset`], but much faster since the data is streamed from local files.
+
+ Contrary to map-style datasets, iterable datasets are lazy and can only be iterated over (e.g. using a for loop).
+ Since they are read sequentially in training loops, iterable datasets are much faster than map-style datasets.
+ All the transformations applied to iterable datasets like filtering or processing are done on-the-fly when you start iterating over the dataset.
+
+ Still, it is possible to shuffle an iterable dataset using [`datasets.IterableDataset.shuffle`].
+ This is a fast approximate shuffling that works best if you have multiple shards and if you specify a buffer size that is big enough.
+
+ To get the best speed performance, make sure your dataset doesn't have an indices mapping.
+ If this is the case, the data are not read contiguously, which can be slow sometimes.
+ You can use `ds = ds.flatten_indices()` to write your dataset in contiguous chunks of data and have optimal speed before switching to an iterable dataset.
+
+ Args:
+ num_shards (`int`, default to `1`):
+ Number of shards to define when instantiating the iterable dataset. This is especially useful for big datasets to be able to shuffle properly,
+ and also to enable fast parallel loading using a PyTorch DataLoader or in distributed setups for example.
+ Shards are defined using [`datasets.Dataset.shard`]: it simply slices the data without writing anything on disk.
+
+ Returns:
+ [`datasets.IterableDataset`]
+
+ Example:
+
+ Basic usage:
+ ```python
+ >>> ids = ds.to_iterable_dataset()
+ >>> for example in ids:
+ ... pass
+ ```
+
+ With lazy filtering and processing:
+ ```python
+ >>> ids = ds.to_iterable_dataset()
+ >>> ids = ids.filter(filter_fn).map(process_fn) # will filter and process on-the-fly when you start iterating over the iterable dataset
+ >>> for example in ids:
+ ... pass
+ ```
+
+ With sharding to enable efficient shuffling:
+ ```python
+ >>> ids = ds.to_iterable_dataset(num_shards=64) # the dataset is split into 64 shards to be iterated over
+ >>> ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer for fast approximate shuffling when you start iterating
+ >>> for example in ids:
+ ... pass
+ ```
+
+ With a PyTorch DataLoader:
+ ```python
+ >>> import torch
+ >>> ids = ds.to_iterable_dataset(num_shards=64)
+ >>> ids = ids.filter(filter_fn).map(process_fn)
+ >>> dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards to each worker to load, filter and process when you start iterating
+ >>> for example in ids:
+ ... pass
+ ```
+
+ With a PyTorch DataLoader and shuffling:
+ ```python
+ >>> import torch
+ >>> ids = ds.to_iterable_dataset(num_shards=64)
+ >>> ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
+ >>> dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from the shuffled list of shards to each worker when you start iterating
+ >>> for example in ids:
+ ... pass
+ ```
+
+ In a distributed setup like PyTorch DDP with a PyTorch DataLoader and shuffling
+ ```python
+ >>> from datasets.distributed import split_dataset_by_node
+ >>> ids = ds.to_iterable_dataset(num_shards=512)
+ >>> ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
+ >>> ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
+ >>> dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
+ >>> for example in ids:
+ ... pass
+ ```
+
+ With shuffling and multiple epochs:
+ ```python
+ >>> ids = ds.to_iterable_dataset(num_shards=64)
+ >>> ids = ids.shuffle(buffer_size=10_000, seed=42) # will shuffle the shards order and use a shuffle buffer when you start iterating
+ >>> for epoch in range(n_epochs):
+ ... ids.set_epoch(epoch) # will use effective_seed = seed + epoch to shuffle the shards and for the shuffle buffer when you start iterating
+ ... for example in ids:
+ ... pass
+ ```
+ Feel free to also use [`IterableDataset.set_epoch`] when using a PyTorch DataLoader or in distributed setups.
+ """
+ from .iterable_dataset import ArrowExamplesIterable, IterableDataset
+
+ if self._format_type is not None:
+ raise NotImplementedError(
+ "Converting a formatted dataset to a formatted iterable dataset is not implemented yet. Please run `my_dataset = my_dataset.with_format(None)` before calling to_iterable_dataset"
+ )
+ if num_shards > len(self):
+ raise ValueError(
+ f"Unable to shard a dataset of size {len(self)} into {num_shards} shards (the number of shards exceeds the number of samples)."
+ )
+ if self._indices is not None:
+ logger.info(
+ "Converting an Arrow dataset to iterable but it has an indices mapping that can make it slower. "
+ "You can use `ds = ds.flatten_indices()` to write your dataset in contiguous chunks of data and have optimal speed."
+ )
+ shards = (
+ [copy.deepcopy(self)]
+ if num_shards == 1
+ else [
+ self.shard(num_shards=num_shards, index=shard_idx, contiguous=True) for shard_idx in range(num_shards)
+ ]
+ )
+ ex_iterable = ArrowExamplesIterable(
+ Dataset._generate_tables_from_shards,
+ kwargs={"shards": shards, "batch_size": config.DEFAULT_MAX_BATCH_SIZE},
+ )
+ return IterableDataset(ex_iterable, info=DatasetInfo(features=self.features))
+
+ def _push_parquet_shards_to_hub(
+ self,
+ repo_id: str,
+ data_dir: str = "data",
+ split: Optional[str] = None,
+ token: Optional[str] = None,
+ revision: Optional[str] = None,
+ create_pr: Optional[bool] = False,
+ max_shard_size: Optional[Union[int, str]] = None,
+ num_shards: Optional[int] = None,
+ embed_external_files: bool = True,
+ ) -> Tuple[str, str, int, int, List[str], int]:
+ """Pushes the dataset shards as Parquet files to the hub.
+
+ Returns:
+ additions (`List[CommitOperation]`): list of the `CommitOperationAdd` of the uploaded shards
+ uploaded_size (`int`): number of uploaded bytes to the repository
+ dataset_nbytes (`int`): approximate size in bytes of the uploaded dataset afer uncompression
+ """
+ # Find decodable columns, because if there are any, we need to:
+ # embed the bytes from the files in the shards
+ decodable_columns = (
+ [k for k, v in self._info.features.items() if require_decoding(v, ignore_decode_attribute=True)]
+ if embed_external_files
+ else []
+ )
+
+ dataset_nbytes = self._estimate_nbytes()
+
+ if num_shards is None:
+ max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
+ num_shards = int(dataset_nbytes / max_shard_size) + 1
+ num_shards = max(num_shards, 1)
+
+ shards = (self.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
+
+ if decodable_columns:
+
+ def shards_with_embedded_external_files(shards):
+ for shard in shards:
+ format = shard.format
+ shard = shard.with_format("arrow")
+ shard = shard.map(
+ embed_table_storage,
+ batched=True,
+ batch_size=1000,
+ keep_in_memory=True,
+ )
+ shard = shard.with_format(**format)
+ yield shard
+
+ shards = shards_with_embedded_external_files(shards)
+
+ api = HfApi(endpoint=config.HF_ENDPOINT, token=token)
+
+ uploaded_size = 0
+ additions = []
+ for index, shard in hf_tqdm(
+ enumerate(shards),
+ desc="Uploading the dataset shards",
+ total=num_shards,
+ ):
+ shard_path_in_repo = f"{data_dir}/{split}-{index:05d}-of-{num_shards:05d}.parquet"
+ buffer = BytesIO()
+ shard.to_parquet(buffer)
+ uploaded_size += buffer.tell()
+ shard_addition = CommitOperationAdd(path_in_repo=shard_path_in_repo, path_or_fileobj=buffer)
+ api.preupload_lfs_files(
+ repo_id=repo_id,
+ additions=[shard_addition],
+ repo_type="dataset",
+ revision=revision,
+ create_pr=create_pr,
+ )
+ additions.append(shard_addition)
+
+ return additions, uploaded_size, dataset_nbytes
+
+ def push_to_hub(
+ self,
+ repo_id: str,
+ config_name: str = "default",
+ set_default: Optional[bool] = None,
+ split: Optional[str] = None,
+ data_dir: Optional[str] = None,
+ commit_message: Optional[str] = None,
+ commit_description: Optional[str] = None,
+ private: Optional[bool] = False,
+ token: Optional[str] = None,
+ revision: Optional[str] = None,
+ branch="deprecated",
+ create_pr: Optional[bool] = False,
+ max_shard_size: Optional[Union[int, str]] = None,
+ num_shards: Optional[int] = None,
+ embed_external_files: bool = True,
+ ) -> CommitInfo:
+ """Pushes the dataset to the hub as a Parquet dataset.
+ The dataset is pushed using HTTP requests and does not need to have neither git or git-lfs installed.
+
+ The resulting Parquet files are self-contained by default. If your dataset contains [`Image`] or [`Audio`]
+ data, the Parquet files will store the bytes of your images or audio files.
+ You can disable this by setting `embed_external_files` to `False`.
+
+ Args:
+ repo_id (`str`):
+ The ID of the repository to push to in the following format: `/` or
+ `/`. Also accepts ``, which will default to the namespace
+ of the logged-in user.
+ config_name (`str`, defaults to "default"):
+ The configuration name (or subset) of a dataset. Defaults to "default".
+ set_default (`bool`, *optional*):
+ Whether to set this configuration as the default one. Otherwise, the default configuration is the one
+ named "default".
+ split (`str`, *optional*):
+ The name of the split that will be given to that dataset. Defaults to `self.split`.
+ data_dir (`str`, *optional*):
+ Directory name that will contain the uploaded data files. Defaults to the `config_name` if different
+ from "default", else "data".
+
+
+ commit_message (`str`, *optional*):
+ Message to commit while pushing. Will default to `"Upload dataset"`.
+ commit_description (`str`, *optional*):
+ Description of the commit that will be created.
+ Additionally, description of the PR if a PR is created (`create_pr` is True).
+
+
+ private (`bool`, *optional*, defaults to `False`):
+ Whether the dataset repository should be set to private or not. Only affects repository creation:
+ a repository that already exists will not be affected by that parameter.
+ token (`str`, *optional*):
+ An optional authentication token for the Hugging Face Hub. If no token is passed, will default
+ to the token saved locally when logging in with `huggingface-cli login`. Will raise an error
+ if no token is passed and the user is not logged-in.
+ revision (`str`, *optional*):
+ Branch to push the uploaded files to. Defaults to the `"main"` branch.
+
+
+ branch (`str`, *optional*):
+ The git branch on which to push the dataset. This defaults to the default branch as specified
+ in your repository, which defaults to `"main"`.
+
+
+
+ `branch` was deprecated in favor of `revision` in version 2.15.0 and will be removed in 3.0.0.
+
+
+ create_pr (`bool`, *optional*, defaults to `False`):
+ Whether to create a PR with the uploaded files or directly commit.
+
+
+ max_shard_size (`int` or `str`, *optional*, defaults to `"500MB"`):
+ The maximum size of the dataset shards to be uploaded to the hub. If expressed as a string, needs to be digits followed by
+ a unit (like `"5MB"`).
+ num_shards (`int`, *optional*):
+ Number of shards to write. By default, the number of shards depends on `max_shard_size`.
+
+
+ embed_external_files (`bool`, defaults to `True`):
+ Whether to embed file bytes in the shards.
+ In particular, this will do the following before the push for the fields of type:
+
+ - [`Audio`] and [`Image`]: remove local path information and embed file content in the Parquet files.
+
+ Return:
+ huggingface_hub.CommitInfo
+
+ Example:
+
+ ```python
+ >>> dataset.push_to_hub("/")
+ >>> dataset_dict.push_to_hub("/", private=True)
+ >>> dataset.push_to_hub("/", max_shard_size="1GB")
+ >>> dataset.push_to_hub("/", num_shards=1024)
+ ```
+
+ If your dataset has multiple splits (e.g. train/validation/test):
+
+ ```python
+ >>> train_dataset.push_to_hub("/", split="train")
+ >>> val_dataset.push_to_hub("/", split="validation")
+ >>> # later
+ >>> dataset = load_dataset("/")
+ >>> train_dataset = dataset["train"]
+ >>> val_dataset = dataset["validation"]
+ ```
+
+ If you want to add a new configuration (or subset) to a dataset (e.g. if the dataset has multiple tasks/versions/languages):
+
+ ```python
+ >>> english_dataset.push_to_hub("/", "en")
+ >>> french_dataset.push_to_hub("/", "fr")
+ >>> # later
+ >>> english_dataset = load_dataset("/", "en")
+ >>> french_dataset = load_dataset("/", "fr")
+ ```
+ """
+ if config_name == "data":
+ raise ValueError("`config_name` cannot be 'data'. Please, choose another name for configuration.")
+
+ if max_shard_size is not None and num_shards is not None:
+ raise ValueError(
+ "Failed to push_to_hub: please specify either max_shard_size or num_shards, but not both."
+ )
+
+ if split is None:
+ split = str(self.split) if self.split is not None else "train"
+
+ if not re.match(_split_re, split):
+ raise ValueError(f"Split name should match '{_split_re}' but got '{split}'.")
+
+ if branch != "deprecated":
+ warnings.warn(
+ "'branch' was deprecated in favor of 'revision' in version 2.15.0 and will be removed in 3.0.0.\n"
+ f"You can remove this warning by passing 'revision={branch}' instead.",
+ FutureWarning,
+ )
+ revision = branch
+
+ api = HfApi(endpoint=config.HF_ENDPOINT, token=token)
+
+ repo_url = api.create_repo(
+ repo_id,
+ token=token,
+ repo_type="dataset",
+ private=private,
+ exist_ok=True,
+ )
+ repo_id = repo_url.repo_id
+
+ if revision is not None:
+ api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
+
+ if not data_dir:
+ data_dir = config_name if config_name != "default" else "data" # for backward compatibility
+
+ additions, uploaded_size, dataset_nbytes = self._push_parquet_shards_to_hub(
+ repo_id=repo_id,
+ data_dir=data_dir,
+ split=split,
+ token=token,
+ revision=revision,
+ max_shard_size=max_shard_size,
+ num_shards=num_shards,
+ create_pr=create_pr,
+ embed_external_files=embed_external_files,
+ )
+
+ # Check if the repo already has a README.md and/or a dataset_infos.json to update them with the new split info (size and pattern)
+ # and delete old split shards (if they exist)
+ repo_with_dataset_card, repo_with_dataset_infos = False, False
+ deletions, deleted_size = [], 0
+ repo_splits = [] # use a list to keep the order of the splits
+ repo_files_to_add = [addition.path_in_repo for addition in additions]
+ for repo_file in api.list_repo_tree(
+ repo_id=repo_id, revision=revision, repo_type="dataset", token=token, recursive=True
+ ):
+ if not isinstance(repo_file, RepoFile):
+ continue
+ if repo_file.rfilename == config.REPOCARD_FILENAME:
+ repo_with_dataset_card = True
+ elif repo_file.rfilename == config.DATASETDICT_INFOS_FILENAME:
+ repo_with_dataset_infos = True
+ elif (
+ repo_file.rfilename.startswith(f"{data_dir}/{split}-") and repo_file.rfilename not in repo_files_to_add
+ ):
+ deletions.append(CommitOperationDelete(path_in_repo=repo_file.rfilename))
+ deleted_size += repo_file.size
+ elif fnmatch.fnmatch(
+ repo_file.rfilename, PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED.replace("{split}", "*")
+ ):
+ repo_split = string_to_dict(
+ repo_file.rfilename,
+ glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED),
+ )["split"]
+ if repo_split not in repo_splits:
+ repo_splits.append(repo_split)
+
+ organization, dataset_name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
+ info_to_dump = self.info.copy()
+ info_to_dump.download_checksums = None
+ info_to_dump.download_size = uploaded_size
+ info_to_dump.dataset_size = dataset_nbytes
+ info_to_dump.size_in_bytes = uploaded_size + dataset_nbytes
+ info_to_dump.config_name = config_name
+ info_to_dump.splits = SplitDict(
+ {split: SplitInfo(split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name)}
+ )
+ # get the info from the README to update them
+ if repo_with_dataset_card:
+ dataset_card_path = api.hf_hub_download(
+ repo_id, config.REPOCARD_FILENAME, repo_type="dataset", revision=revision
+ )
+ dataset_card = DatasetCard.load(Path(dataset_card_path))
+ dataset_card_data = dataset_card.data
+ metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data)
+ dataset_infos: DatasetInfosDict = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
+ if dataset_infos and config_name in dataset_infos:
+ repo_info = dataset_infos[config_name]
+ else:
+ repo_info = None
+ # get the deprecated dataset_infos.json to update them
+ elif repo_with_dataset_infos:
+ dataset_card = None
+ dataset_card_data = DatasetCardData()
+ metadata_configs = MetadataConfigs()
+ dataset_infos_path = api.hf_hub_download(
+ repo_id, config.DATASETDICT_INFOS_FILENAME, repo_type="dataset", revision=revision
+ )
+ with open(dataset_infos_path, encoding="utf-8") as f:
+ dataset_infos: dict = json.load(f)
+ dataset_info = dataset_infos.get(config_name, None) if dataset_infos else None
+ repo_info = DatasetInfo.from_dict(dataset_info) if dataset_info else None
+ else:
+ dataset_card = None
+ dataset_card_data = DatasetCardData()
+ metadata_configs = MetadataConfigs()
+ repo_info = None
+ # update the total info to dump from existing info
+ if repo_info is not None:
+ logger.info("Updating downloaded metadata with the new split.")
+ if repo_info.splits and list(repo_info.splits) != [split]:
+ if self._info.features != repo_info.features:
+ raise ValueError(
+ f"Features of the new split don't match the features of the existing splits on the hub: {self._info.features} != {repo_info.features}"
+ )
+
+ if split in repo_info.splits:
+ repo_info.download_size -= deleted_size
+ repo_info.dataset_size -= repo_info.splits.get(split, SplitInfo()).num_bytes or 0
+
+ repo_info.download_checksums = None
+ repo_info.download_size = (repo_info.download_size or 0) + uploaded_size
+ repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes
+ repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size
+ repo_info.splits.pop(split, None)
+ repo_info.splits[split] = SplitInfo(
+ split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name
+ )
+ info_to_dump = repo_info
+ # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed
+ if not metadata_configs and repo_splits:
+ default_metadata_configs_to_dump = {
+ "data_files": [{"split": split, "path": f"data/{split}-*"} for split in repo_splits]
+ }
+ MetadataConfigs({"default": default_metadata_configs_to_dump}).to_dataset_card_data(dataset_card_data)
+ # update the metadata configs
+ if config_name in metadata_configs:
+ metadata_config = metadata_configs[config_name]
+ if "data_files" in metadata_config:
+ data_files_to_dump = sanitize_patterns(metadata_config["data_files"])
+ else:
+ data_files_to_dump = {}
+ # add the new split
+ data_files_to_dump[split] = [f"{data_dir}/{split}-*"]
+ metadata_config_to_dump = {
+ "data_files": [
+ {
+ "split": _split,
+ "path": _pattern[0] if len(_pattern) == 1 else _pattern,
+ }
+ for _split, _pattern in data_files_to_dump.items()
+ ]
+ }
+ else:
+ metadata_config_to_dump = {"data_files": [{"split": split, "path": f"{data_dir}/{split}-*"}]}
+ if set_default and config_name != "default":
+ if metadata_configs:
+ default_config_name = metadata_configs.get_default_config_name()
+ if default_config_name == "default":
+ raise ValueError(
+ "There exists a configuration named 'default'. To set a different configuration as default, "
+ "rename the 'default' one first."
+ )
+ else:
+ _ = metadata_configs[default_config_name].pop("default")
+ metadata_config_to_dump["default"] = True
+ # push to the deprecated dataset_infos.json
+ if repo_with_dataset_infos:
+ dataset_infos_path = api.hf_hub_download(
+ repo_id, config.DATASETDICT_INFOS_FILENAME, repo_type="dataset", revision=revision
+ )
+ with open(dataset_infos_path, encoding="utf-8") as f:
+ dataset_infos: dict = json.load(f)
+ dataset_infos[config_name] = asdict(info_to_dump)
+ buffer = BytesIO()
+ buffer.write(json.dumps(dataset_infos, indent=4).encode("utf-8"))
+ additions.append(
+ CommitOperationAdd(path_in_repo=config.DATASETDICT_INFOS_FILENAME, path_or_fileobj=buffer)
+ )
+ # push to README
+ DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
+ MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
+ dataset_card = DatasetCard(f"---\n{dataset_card_data}\n---\n") if dataset_card is None else dataset_card
+ additions.append(
+ CommitOperationAdd(path_in_repo=config.REPOCARD_FILENAME, path_or_fileobj=str(dataset_card).encode())
+ )
+
+ commit_message = commit_message if commit_message is not None else "Upload dataset"
+ if len(additions) <= config.UPLOADS_MAX_NUMBER_PER_COMMIT:
+ commit_info = api.create_commit(
+ repo_id,
+ operations=additions + deletions,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ token=token,
+ repo_type="dataset",
+ revision=revision,
+ create_pr=create_pr,
+ )
+ else:
+ logger.info(
+ f"Number of files to upload is larger than {config.UPLOADS_MAX_NUMBER_PER_COMMIT}. Splitting the push into multiple commits."
+ )
+ num_commits = math.ceil(len(additions) / config.UPLOADS_MAX_NUMBER_PER_COMMIT)
+ for i in range(0, num_commits):
+ operations = additions[
+ i * config.UPLOADS_MAX_NUMBER_PER_COMMIT : (i + 1) * config.UPLOADS_MAX_NUMBER_PER_COMMIT
+ ] + (deletions if i == 0 else [])
+ commit_info = api.create_commit(
+ repo_id,
+ operations=operations,
+ commit_message=commit_message + f" (part {i:05d}-of-{num_commits:05d})",
+ commit_description=commit_description,
+ token=token,
+ repo_type="dataset",
+ revision=revision,
+ create_pr=create_pr,
+ )
+ logger.info(
+ f"Commit #{i+1} completed"
+ + (f" (still {num_commits - i - 1} to go)" if num_commits - i - 1 else "")
+ + "."
+ )
+ return commit_info
+
+ @transmit_format
+ @fingerprint_transform(inplace=False)
+ def add_column(self, name: str, column: Union[list, np.array], new_fingerprint: str):
+ """Add column to Dataset.
+
+
+
+ Args:
+ name (`str`):
+ Column name.
+ column (`list` or `np.array`):
+ Column data to be added.
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> more_text = ds["text"]
+ >>> ds.add_column(name="text_2", column=more_text)
+ Dataset({
+ features: ['text', 'label', 'text_2'],
+ num_rows: 1066
+ })
+ ```
+ """
+ column_table = InMemoryTable.from_pydict({name: column})
+ _check_column_names(self._data.column_names + column_table.column_names)
+ dataset = self.flatten_indices() if self._indices is not None else self
+ # Concatenate tables horizontally
+ table = concat_tables([dataset._data, column_table], axis=1)
+ # Update features
+ info = dataset.info.copy()
+ info.features.update(Features.from_arrow_schema(column_table.schema))
+ table = update_metadata_with_features(table, info.features)
+ return Dataset(table, info=info, split=self.split, indices_table=None, fingerprint=new_fingerprint)
+
+ def add_faiss_index(
+ self,
+ column: str,
+ index_name: Optional[str] = None,
+ device: Optional[int] = None,
+ string_factory: Optional[str] = None,
+ metric_type: Optional[int] = None,
+ custom_index: Optional["faiss.Index"] = None, # noqa: F821
+ batch_size: int = 1000,
+ train_size: Optional[int] = None,
+ faiss_verbose: bool = False,
+ dtype=np.float32,
+ ):
+ """Add a dense index using Faiss for fast retrieval.
+ By default the index is done over the vectors of the specified column.
+ You can specify `device` if you want to run it on GPU (`device` must be the GPU index).
+ You can find more information about Faiss here:
+
+ - For [string factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)
+
+ Args:
+ column (`str`):
+ The column of the vectors to add to the index.
+ index_name (`str`, *optional*):
+ The `index_name`/identifier of the index.
+ This is the `index_name` that is used to call [`~datasets.Dataset.get_nearest_examples`] or [`~datasets.Dataset.search`].
+ By default it corresponds to `column`.
+ device (`Union[int, List[int]]`, *optional*):
+ If positive integer, this is the index of the GPU to use. If negative integer, use all GPUs.
+ If a list of positive integers is passed in, run only on those GPUs. By default it uses the CPU.
+ string_factory (`str`, *optional*):
+ This is passed to the index factory of Faiss to create the index.
+ Default index class is `IndexFlat`.
+ metric_type (`int`, *optional*):
+ Type of metric. Ex: `faiss.METRIC_INNER_PRODUCT` or `faiss.METRIC_L2`.
+ custom_index (`faiss.Index`, *optional*):
+ Custom Faiss index that you already have instantiated and configured for your needs.
+ batch_size (`int`):
+ Size of the batch to use while adding vectors to the `FaissIndex`. Default value is `1000`.
+
+ train_size (`int`, *optional*):
+ If the index needs a training step, specifies how many vectors will be used to train the index.
+ faiss_verbose (`bool`, defaults to `False`):
+ Enable the verbosity of the Faiss index.
+ dtype (`data-type`):
+ The dtype of the numpy arrays that are indexed.
+ Default is `np.float32`.
+
+ Example:
+
+ ```python
+ >>> ds = datasets.load_dataset('crime_and_punish', split='train')
+ >>> ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))
+ >>> ds_with_embeddings.add_faiss_index(column='embeddings')
+ >>> # query
+ >>> scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)
+ >>> # save index
+ >>> ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')
+
+ >>> ds = datasets.load_dataset('crime_and_punish', split='train')
+ >>> # load index
+ >>> ds.load_faiss_index('embeddings', 'my_index.faiss')
+ >>> # query
+ >>> scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
+ ```
+ """
+ with self.formatted_as(type="numpy", columns=[column], dtype=dtype):
+ super().add_faiss_index(
+ column=column,
+ index_name=index_name,
+ device=device,
+ string_factory=string_factory,
+ metric_type=metric_type,
+ custom_index=custom_index,
+ batch_size=batch_size,
+ train_size=train_size,
+ faiss_verbose=faiss_verbose,
+ )
+ return self
+
+ def add_faiss_index_from_external_arrays(
+ self,
+ external_arrays: np.array,
+ index_name: str,
+ device: Optional[int] = None,
+ string_factory: Optional[str] = None,
+ metric_type: Optional[int] = None,
+ custom_index: Optional["faiss.Index"] = None, # noqa: F821
+ batch_size: int = 1000,
+ train_size: Optional[int] = None,
+ faiss_verbose: bool = False,
+ dtype=np.float32,
+ ):
+ """Add a dense index using Faiss for fast retrieval.
+ The index is created using the vectors of `external_arrays`.
+ You can specify `device` if you want to run it on GPU (`device` must be the GPU index).
+ You can find more information about Faiss here:
+
+ - For [string factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)
+
+ Args:
+ external_arrays (`np.array`):
+ If you want to use arrays from outside the lib for the index, you can set `external_arrays`.
+ It will use `external_arrays` to create the Faiss index instead of the arrays in the given `column`.
+ index_name (`str`):
+ The `index_name`/identifier of the index.
+ This is the `index_name` that is used to call [`~datasets.Dataset.get_nearest_examples`] or [`~datasets.Dataset.search`].
+ device (Optional `Union[int, List[int]]`, *optional*):
+ If positive integer, this is the index of the GPU to use. If negative integer, use all GPUs.
+ If a list of positive integers is passed in, run only on those GPUs. By default it uses the CPU.
+ string_factory (`str`, *optional*):
+ This is passed to the index factory of Faiss to create the index.
+ Default index class is `IndexFlat`.
+ metric_type (`int`, *optional*):
+ Type of metric. Ex: `faiss.faiss.METRIC_INNER_PRODUCT` or `faiss.METRIC_L2`.
+ custom_index (`faiss.Index`, *optional*):
+ Custom Faiss index that you already have instantiated and configured for your needs.
+ batch_size (`int`, *optional*):
+ Size of the batch to use while adding vectors to the FaissIndex. Default value is 1000.
+
+ train_size (`int`, *optional*):
+ If the index needs a training step, specifies how many vectors will be used to train the index.
+ faiss_verbose (`bool`, defaults to False):
+ Enable the verbosity of the Faiss index.
+ dtype (`numpy.dtype`):
+ The dtype of the numpy arrays that are indexed. Default is np.float32.
+ """
+ super().add_faiss_index_from_external_arrays(
+ external_arrays=external_arrays.astype(dtype),
+ index_name=index_name,
+ device=device,
+ string_factory=string_factory,
+ metric_type=metric_type,
+ custom_index=custom_index,
+ batch_size=batch_size,
+ train_size=train_size,
+ faiss_verbose=faiss_verbose,
+ )
+
+ def add_elasticsearch_index(
+ self,
+ column: str,
+ index_name: Optional[str] = None,
+ host: Optional[str] = None,
+ port: Optional[int] = None,
+ es_client: Optional["elasticsearch.Elasticsearch"] = None, # noqa: F821
+ es_index_name: Optional[str] = None,
+ es_index_config: Optional[dict] = None,
+ ):
+ """Add a text index using ElasticSearch for fast retrieval. This is done in-place.
+
+ Args:
+ column (`str`):
+ The column of the documents to add to the index.
+ index_name (`str`, *optional*):
+ The `index_name`/identifier of the index.
+ This is the index name that is used to call [`~Dataset.get_nearest_examples`] or [`~Dataset.search`].
+ By default it corresponds to `column`.
+ host (`str`, *optional*, defaults to `localhost`):
+ Host of where ElasticSearch is running.
+ port (`str`, *optional*, defaults to `9200`):
+ Port of where ElasticSearch is running.
+ es_client (`elasticsearch.Elasticsearch`, *optional*):
+ The elasticsearch client used to create the index if host and port are `None`.
+ es_index_name (`str`, *optional*):
+ The elasticsearch index name used to create the index.
+ es_index_config (`dict`, *optional*):
+ The configuration of the elasticsearch index.
+ Default config is:
+ ```
+ {
+ "settings": {
+ "number_of_shards": 1,
+ "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
+ },
+ "mappings": {
+ "properties": {
+ "text": {
+ "type": "text",
+ "analyzer": "standard",
+ "similarity": "BM25"
+ },
+ }
+ },
+ }
+ ```
+ Example:
+
+ ```python
+ >>> es_client = elasticsearch.Elasticsearch()
+ >>> ds = datasets.load_dataset('crime_and_punish', split='train')
+ >>> ds.add_elasticsearch_index(column='line', es_client=es_client, es_index_name="my_es_index")
+ >>> scores, retrieved_examples = ds.get_nearest_examples('line', 'my new query', k=10)
+ ```
+ """
+ with self.formatted_as(type=None, columns=[column]):
+ super().add_elasticsearch_index(
+ column=column,
+ index_name=index_name,
+ host=host,
+ port=port,
+ es_client=es_client,
+ es_index_name=es_index_name,
+ es_index_config=es_index_config,
+ )
+ return self
+
+ @transmit_format
+ @fingerprint_transform(inplace=False)
+ def add_item(self, item: dict, new_fingerprint: str):
+ """Add item to Dataset.
+
+
+
+ Args:
+ item (`dict`):
+ Item data to be added.
+
+ Returns:
+ [`Dataset`]
+
+ Example:
+
+ ```py
+ >>> from datasets import load_dataset
+ >>> ds = load_dataset("rotten_tomatoes", split="validation")
+ >>> new_review = {'label': 0, 'text': 'this movie is the absolute worst thing I have ever seen'}
+ >>> ds = ds.add_item(new_review)
+ >>> ds[-1]
+ {'label': 0, 'text': 'this movie is the absolute worst thing I have ever seen'}
+ ```
+ """
+ item_table = InMemoryTable.from_pydict({k: [v] for k, v in item.items()})
+ # We don't call _check_if_features_can_be_aligned here so this cast is "unsafe"
+ dset_features, item_features = _align_features(
+ [self._info.features, Features.from_arrow_schema(item_table.schema)]
+ )
+ # Cast to align the schemas of the tables and concatenate the tables
+ table = concat_tables(
+ [
+ self._data.cast(dset_features.arrow_schema) if self._info.features != dset_features else self._data,
+ item_table.cast(item_features.arrow_schema),
+ ]
+ )
+ if self._indices is None:
+ indices_table = None
+ else:
+ item_indices_array = pa.array([len(self._data)], type=pa.uint64())
+ item_indices_table = InMemoryTable.from_arrays([item_indices_array], names=["indices"])
+ indices_table = concat_tables([self._indices, item_indices_table])
+ info = self.info.copy()
+ info.features.update(item_features)
+ table = update_metadata_with_features(table, info.features)
+ return Dataset(
+ table,
+ info=info,
+ split=self.split,
+ indices_table=indices_table,
+ fingerprint=new_fingerprint,
+ )
+
+ def align_labels_with_mapping(self, label2id: Dict, label_column: str) -> "Dataset":
+ """Align the dataset's label ID and label name mapping to match an input `label2id` mapping.
+ This is useful when you want to ensure that a model's predicted labels are aligned with the dataset.
+ The alignment in done using the lowercase label names.
+
+ Args:
+ label2id (`dict`):
+ The label name to ID mapping to align the dataset with.
+ label_column (`str`):
+ The column name of labels to align on.
+
+ Example:
+
+ ```python
+ >>> # dataset with mapping {'entailment': 0, 'neutral': 1, 'contradiction': 2}
+ >>> ds = load_dataset("glue", "mnli", split="train")
+ >>> # mapping to align with
+ >>> label2id = {'CONTRADICTION': 0, 'NEUTRAL': 1, 'ENTAILMENT': 2}
+ >>> ds_aligned = ds.align_labels_with_mapping(label2id, "label")
+ ```
+
+ """
+ # Sanity checks
+ if label_column not in self._data.column_names:
+ raise ValueError(f"Column ({label_column}) not in table columns ({self._data.column_names}).")
+
+ label_feature = self._info.features[label_column]
+ if not (
+ isinstance(label_feature, ClassLabel)
+ or (isinstance(label_feature, Sequence) and isinstance(label_feature.feature, ClassLabel))
+ ):
+ raise ValueError(
+ f"Aligning labels with a mapping is only supported for {ClassLabel.__name__} column or {Sequence.__name__} column with the inner type {ClassLabel.__name__}, and column {label_feature} is of type {type(label_feature).__name__}."
+ )
+
+ # Sort input mapping by ID value to ensure the label names are aligned
+ label2id = dict(sorted(label2id.items(), key=lambda item: item[1]))
+ label_names = list(label2id.keys())
+ # Some label mappings use uppercase label names so we lowercase them during alignment
+ label2id = {k.lower(): v for k, v in label2id.items()}
+ int2str_function = (
+ label_feature.int2str if isinstance(label_feature, ClassLabel) else label_feature.feature.int2str
+ )
+
+ if isinstance(label_feature, ClassLabel):
+
+ def process_label_ids(batch):
+ dset_label_names = [
+ int2str_function(label_id).lower() if label_id is not None else None
+ for label_id in batch[label_column]
+ ]
+ batch[label_column] = [
+ label2id[label_name] if label_name is not None else None for label_name in dset_label_names
+ ]
+ return batch
+
+ else:
+
+ def process_label_ids(batch):
+ dset_label_names = [
+ [int2str_function(label_id).lower() if label_id is not None else None for label_id in seq]
+ for seq in batch[label_column]
+ ]
+ batch[label_column] = [
+ [label2id[label_name] if label_name is not None else None for label_name in seq]
+ for seq in dset_label_names
+ ]
+ return batch
+
+ features = self.features
+ features[label_column] = (
+ ClassLabel(num_classes=len(label_names), names=label_names)
+ if isinstance(label_feature, ClassLabel)
+ else Sequence(ClassLabel(num_classes=len(label_names), names=label_names))
+ )
+ return self.map(process_label_ids, features=features, batched=True, desc="Aligning the labels")
+
+
+def _concatenate_map_style_datasets(
+ dsets: List[Dataset],
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ axis: int = 0,
+):
+ """
+ Converts a list of :class:`Dataset` with the same schema into a single :class:`Dataset`.
+ When you concatenate on axis 0, missing data are filled with None values.
+
+ Args:
+ dsets (`List[datasets.Dataset]`): List of Datasets to concatenate.
+ info (:class:`DatasetInfo`, optional): Dataset information, like description, citation, etc.
+ split (:class:`NamedSplit`, optional): Name of the dataset split.
+ axis (``{0, 1}``, default ``0``, meaning over rows):
+ Axis to concatenate over, where ``0`` means over rows (vertically) and ``1`` means over columns
+ (horizontally).
+
+ *New in version 1.6.0*
+
+ Example:
+
+ ```py
+ >>> ds3 = _concatenate_map_style_datasets([ds1, ds2])
+ ```
+ """
+ # Ignore datasets with no rows
+ if any(dset.num_rows > 0 for dset in dsets):
+ dsets = [dset for dset in dsets if dset.num_rows > 0]
+ else:
+ # Return first dataset if all datasets are empty
+ return dsets[0]
+
+ # Perform checks (and a potentional cast if axis=0)
+ if axis == 0:
+ _check_if_features_can_be_aligned([dset.features for dset in dsets])
+ else:
+ if not all(dset.num_rows == dsets[0].num_rows for dset in dsets):
+ raise ValueError("Number of rows must match for all datasets")
+ _check_column_names([col_name for dset in dsets for col_name in dset._data.column_names])
+
+ # Find common format or reset format
+ format = dsets[0].format
+ if any(dset.format != format for dset in dsets):
+ format = {}
+ logger.info("Some of the datasets have disparate format. Resetting the format of the concatenated dataset.")
+
+ def apply_offset_to_indices_table(table, offset):
+ if offset == 0:
+ return table
+ else:
+ array = table["indices"]
+ new_array = pc.add(array, pa.scalar(offset, type=pa.uint64()))
+ return InMemoryTable.from_arrays([new_array], names=["indices"])
+
+ # Concatenate indices if they exist
+ if any(dset._indices is not None for dset in dsets):
+ if axis == 0:
+ # Datasets with no indices tables are replaced with a dataset with an indices table in memory.
+ # Applying an offset to an indices table also brings the table in memory.
+ indices_tables = []
+ for i in range(len(dsets)):
+ if dsets[i]._indices is None:
+ dsets[i] = dsets[i]._select_with_indices_mapping(range(len(dsets[i])))
+ indices_tables.append(dsets[i]._indices)
+
+ # An offset needs to be applied to the indices before concatenating
+ offset = 0
+ for i in range(len(dsets)):
+ indices_tables[i] = apply_offset_to_indices_table(indices_tables[i], offset)
+ offset += len(dsets[i]._data)
+
+ # Concatenate indices
+ indices_tables = [t for t in indices_tables if len(t) > 0]
+ if indices_tables:
+ indices_table = concat_tables(indices_tables)
+ else:
+ indices_table = InMemoryTable.from_batches([], schema=pa.schema({"indices": pa.int64()}))
+ else:
+ if len(dsets) == 1:
+ indices_table = dsets[0]._indices
+ else:
+ for i in range(len(dsets)):
+ dsets[i] = dsets[i].flatten_indices()
+ indices_table = None
+ else:
+ indices_table = None
+
+ table = concat_tables([dset._data for dset in dsets], axis=axis)
+ if axis == 0:
+ features_list = _align_features([dset.features for dset in dsets])
+ else:
+ features_list = [dset.features for dset in dsets]
+ table = update_metadata_with_features(table, {k: v for features in features_list for k, v in features.items()})
+
+ # Concatenate infos
+ if info is None:
+ info = DatasetInfo.from_merge([dset.info for dset in dsets])
+ fingerprint = update_fingerprint(
+ "".join(dset._fingerprint for dset in dsets), _concatenate_map_style_datasets, {"info": info, "split": split}
+ )
+
+ # Make final concatenated dataset
+ concatenated_dataset = Dataset(
+ table,
+ info=info,
+ split=split,
+ indices_table=indices_table,
+ fingerprint=fingerprint,
+ )
+ concatenated_dataset.set_format(**format)
+ return concatenated_dataset
+
+
+def _interleave_map_style_datasets(
+ datasets: List["Dataset"],
+ probabilities: Optional[List[float]] = None,
+ seed: Optional[int] = None,
+ info: Optional[DatasetInfo] = None,
+ split: Optional[NamedSplit] = None,
+ stopping_strategy: Literal["first_exhausted", "all_exhausted"] = "first_exhausted",
+ **kwargs,
+) -> "Dataset":
+ """
+ Interleave several map-style datasets (sources) into a single map-style dataset.
+ The new dataset is constructed by alternating between the sources to get the examples.
+ If `probabilities = None` (default) the new dataset is constructed by cycling between each source to get the examples.
+ If `probabilities` is not `None, the new dataset is constructed by getting examples from a random source at a time according to the provided probabilities.
+
+ Args:
+ datasets (`List[Dataset]`): list of datasets to interleave
+ probabilities (`List[float]`, optional, default None): If specified, the new dataset is constructed by sampling
+ examples from one source at a time according to these probabilities.
+ seed (`int`, optional, default None): The random seed used to choose a source for each example.
+ info (:class:`DatasetInfo`, optional): Dataset information, like description, citation, etc.
+ split (:class:`NamedSplit`, optional): Name of the dataset split.
+ stopping_strategy (`str`, defaults to `first_exhausted`):
+ Two strategies are proposed right now.
+ By default, `first_exhausted` is an undersampling strategy, i.e the dataset construction is stopped as soon as one dataset has ran out of samples.
+ If the strategy is `all_exhausted`, we use an oversampling strategy, i.e the dataset construction is stopped as soon as every samples of every dataset has been added at least once.
+ Note that if the strategy is `all_exhausted`, the interleaved dataset size can get enormous:
+ - with no probabilities, the resulting dataset will have max_length_datasets*nb_dataset samples.
+ - with given probabilities, the resulting dataset will have more samples if some datasets have really low probability of visiting.
+ **kwargs (additional keyword arguments): Keyword arguments to be passed to :meth:`datasets.Datasets.select` when selecting the indices used to interleave the datasets.
+
+ Output:
+ :class:`datasets.Dataset`
+ """
+ if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
+ raise ValueError(
+ f"{stopping_strategy} stopping strategy in `interleave_datasets` is not implemented yet with a list of {type(datasets[0])}"
+ )
+
+ # To interleave the datasets, we concatenate them and then we re-order the indices
+ concatenated_datasets = _concatenate_map_style_datasets(datasets, info=info, split=split)
+
+ # Let's now build the indices to pass to .select()
+ lengths = [len(dset) for dset in datasets]
+ offsets = np.cumsum([0] + lengths[:-1])
+
+ # if stopping_strategy is "first_exhausted", it is an undersampling situation whereas it is an oversampling situation if it is "all_exhausted"
+ oversampling = stopping_strategy == "all_exhausted"
+
+ if probabilities is None and not oversampling:
+ # Undersampling situation with cycling between each sources
+ # Example:: If lengths of the datasets are [3, 4, 5]
+ # Then the resulting indices should be [0, 3, 7, 1, 4, 8, 2, 6, 9]
+ # Note that we only have 3 examples per dataset since the first dataset ran out of examples
+
+ # Reasoning behind the following operation: keeping the min_length first indices of each dataset
+ # while offsetting in order to correspond to the right indices of the concatenated dataset
+ # and flattening to effectively interleave the datasets
+ indices = (offsets.reshape(1, -1) + np.arange(min(lengths)).reshape(-1, 1)).flatten().tolist()
+ elif probabilities is None:
+ # Oversampling situation with cycling between each sources
+ # Then the resulting indices should be [0, 3, 7, 1, 4, 8, 2, 5, 9, 0, 6, 10, 1, 3, 11]
+ # Note that we have 5 examples per dataset with a rolling window since the longest dataset has 5 samples
+
+ # Reasoning behind the following operation: for each dataset indices (i.e column) repeat the indices to have max_length indices per dataset
+ # For example, if the max_length is 5 and the i-th dataset has 3 samples, the i-th column will be [0,1,2,0,1]
+ indices = np.mod(np.arange(max(lengths)).reshape(-1, 1), np.array(lengths).reshape(1, -1))
+
+ # We have to keep the indices to their respective dataset offsets and to flatten to effectively interleave the datasets
+ indices = (indices + offsets).flatten().tolist()
+
+ else:
+ # boolean array indicating if at index i if the dataset_i has been fully exhausted
+ is_exhausted = np.full(len(lengths), False)
+
+ # if undersampling ("first_exhausted"), we stop as soon as one dataset is exhausted
+ # if oversampling ("all_exhausted"), we stop as soons as every dataset is exhausted, i.e as soon as every samples of every dataset has been visited at least once
+ bool_strategy_func = np.all if oversampling else np.any
+
+ def iter_random_indices():
+ """Get an infinite iterator that randomly samples the index of the source to pick examples from."""
+ rng = np.random.default_rng(seed)
+ while True:
+ yield from (int(i) for i in rng.choice(len(datasets), size=1000, p=probabilities))
+
+ current_index = [0] * len(datasets)
+ indices = []
+ for source_idx in iter_random_indices():
+ # If no oversampling, we stop as soon as a dataset has ran out of examples (np.any)
+ # Otherwise, we stop as soon as every dataset has ran out of examples (np.all)
+ if bool_strategy_func(is_exhausted):
+ # the stopping condition was reached, let's stop
+ break
+
+ # let's add the example at the current index of the `source_idx`-th dataset
+ indices.append(current_index[source_idx] + offsets[source_idx])
+ current_index[source_idx] += 1
+
+ # we've ran out of examples for the current dataset, let's update our boolean array and bring the current_index back to 0
+ if current_index[source_idx] >= lengths[source_idx]:
+ is_exhausted[source_idx] = True
+ current_index[source_idx] = 0
+
+ return concatenated_datasets.select(indices, **kwargs)
+
+
+def _split_by_node_map_style_dataset(dataset: Dataset, rank: int, world_size: int) -> Dataset:
+ """
+ Split a dataset for the node at rank `rank` in a pool of nodes of size `world_size`.
+ Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
+ To maximize data loading throughput, chunks are made of contiguous data on disk if possible.
+
+ Args:
+ dataset ([`Dataset`]):
+ The dataset to split by node.
+ rank (`int`):
+ Rank of the current node.
+ world_size (`int`):
+ Total number of nodes.
+
+ Returns:
+ [`Dataset`]: The dataset to be used on the node at rank `rank`.
+ """
+ return dataset.shard(num_shards=world_size, index=rank, contiguous=True)
+
+
+# This is outside Dataset.filter as it needs to be picklable for multiprocessing
+
+
+def get_indices_from_mask_function(
+ function: Callable,
+ batched: bool,
+ with_indices: bool,
+ with_rank: bool,
+ input_columns: Optional[Union[str, List[str]]],
+ indices_mapping: Optional[Table] = None,
+ *args,
+ **fn_kwargs,
+):
+ if batched:
+ # we extract indices and rank from args
+ *inputs, indices, rank = args
+ additional_args = ()
+ if with_indices:
+ additional_args += (indices,)
+ if with_rank:
+ additional_args += (rank,)
+ mask = function(*inputs, *additional_args, **fn_kwargs)
+ else:
+ # we get batched data (to do less look-ups) but `function` only accepts one example
+ # therefore we need to call `function` on each example of the batch to get the mask
+ *inputs, indices, rank = args
+ mask = []
+ if input_columns is None:
+ # inputs only contains a batch of examples
+ batch: dict = inputs[0]
+ num_examples = len(batch[next(iter(batch.keys()))])
+ for i in range(num_examples):
+ example = {key: batch[key][i] for key in batch}
+ additional_args = ()
+ if with_indices:
+ additional_args += (indices[i],)
+ if with_rank:
+ additional_args += (rank,)
+ mask.append(function(example, *additional_args, **fn_kwargs))
+ else:
+ # inputs is a list of columns
+ columns: List[List] = inputs
+ num_examples = len(columns[0])
+ for i in range(num_examples):
+ input = [column[i] for column in columns]
+ additional_args = ()
+ if with_indices:
+ additional_args += (indices[i],)
+ if with_rank:
+ additional_args += (rank,)
+ mask.append(function(*input, *additional_args, **fn_kwargs))
+ indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
+ if indices_mapping is not None:
+ indices_array = pa.array(indices_array, type=pa.uint64())
+ indices_array = indices_mapping.column(0).take(indices_array)
+ indices_array = indices_array.to_pylist()
+ return {"indices": indices_array}