fixes

Signed-off-by: amogkam <[email protected]>

python/ray/data/dataset.py

@@ -2444,7 +2444,7 @@ def write_parquet(
  path: The path to the destination root directory, where
  parquet files are written to.
  filesystem: The pyarrow filesystem implementation to write to.
- These filesystems are specified in the 
+ These filesystems are specified in the
  `pyarrow docs <https://arrow.apache.org/docs\
  /python/api/filesystems.html#filesystem-implementations>`_.
  Specify this if you need to provide specific configurations to the
@@ -2962,7 +2962,7 @@ def write_mongo(
  uri: The URI to the destination MongoDB where the dataset is
  written to. For the URI format, see details in the
  `MongoDB docs <https://www.mongodb.com/docs/manual/reference\
- connection-string/>`_.
+ /connection-string/>`_.
  database: The name of the database. This database must exist otherwise
  a ValueError is raised.
  collection: The name of the collection in the database. This collection

python/ray/data/read_api.py

@@ -495,13 +495,13 @@ def read_mongo(
  Args:
  uri: The URI of the source MongoDB where the dataset is
  read from. For the URI format, see details in the `MongoDB docs <https:/\
- www.mongodb.com/docs/manual/reference/connection-string/>`_.
+ /www.mongodb.com/docs/manual/reference/connection-string/>`_.
  database: The name of the database hosted in the MongoDB. This database
  must exist otherwise ValueError is raised.
  collection: The name of the collection in the database. This collection
  must exist otherwise ValueError is raised.
  pipeline: A `MongoDB pipeline <https://www.mongodb.com/docs/manual/core\
- aggregation-pipeline/>`_, which is executed on the given collection
+ /aggregation-pipeline/>`_, which is executed on the given collection
  with results used to create Dataset. If None, the entire collection will
  be read.
  schema: The schema used to read the collection. If None, it'll be inferred from
@@ -514,7 +514,7 @@ def read_mongo(
  <read_parallelism>`.
  ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks.
  mongo_args: kwargs passed to `aggregate_arrow_all() <https://mongo-arrow\
- readthedocs.io/en/pymongoarrow-0.1.1/api/api.html#pymongoarrow.api\
+ .readthedocs.io/en/latest/api/api.html#pymongoarrow.api\
  aggregate_arrow_all>`_ in pymongoarrow in producing
  Arrow-formatted results.
 
@@ -1104,7 +1104,7 @@ def read_csv(
  By default, :meth:`~ray.data.read_csv` reads all files from file paths. If you want to filter
  files by file extensions, set the ``partition_filter`` parameter.
 
- Read only *.csv files from a directory.
+ Read only ``*.csv`` files from a directory.
 
  >>> from ray.data.datasource import FileExtensionFilter
  >>> ray.data.read_csv("example:https://different-extensions/",
@@ -2065,33 +2065,52 @@ def from_huggingface(
 
  Example:
 
- .. doctest::
+ ..
+ The following `testoutput` is mocked to avoid illustrating download
+ logs like "Downloading and preparing dataset 162.17 MiB".
 
- >>> import ray
- >>> import datasets
- >>> hf_dataset = datasets.load_dataset("tweet_eval", "emotion")
- >>> ray_ds = ray.data.from_huggingface(hf_dataset)
- >>> ray_ds
- {'train': MaterializedDataset(
- num_blocks=...,
- num_rows=3257,
- schema={text: string, label: int64}
- ), 'test': MaterializedDataset(
- num_blocks=...,
- num_rows=1421,
- schema={text: string, label: int64}
- ), 'validation': MaterializedDataset(
- num_blocks=...,
- num_rows=374,
- schema={text: string, label: int64}
- )}
- >>> ray_ds = ray.data.from_huggingface(hf_dataset["train"])
- >>> ray_ds
- MaterializedDataset(
- num_blocks=...,
- num_rows=3257,
- schema={text: string, label: int64}
- )
+ .. testcode::
+
+ import ray
+ import datasets
+
+ hf_dataset = datasets.load_dataset("tweet_eval", "emotion")
+ ray_ds = ray.data.from_huggingface(hf_dataset)
+
+ print(ray_ds)
+
+ .. testoutput::
+ :options: +MOCK
+
+ {'train': MaterializedDataset(
+ num_blocks=...,
+ num_rows=3257,
+ schema={text: string, label: int64}
+ ), 'test': MaterializedDataset(
+ num_blocks=...,
+ num_rows=1421,
+ schema={text: string, label: int64}
+ ), 'validation': MaterializedDataset(
+ num_blocks=...,
+ num_rows=374,
+ schema={text: string, label: int64}
+ )}
+
+ Load only a single split of the Huggingface Dataset.
+
+ .. testcode::
+
+ ray_ds = ray.data.from_huggingface(hf_dataset["train"])
+ print(ray_ds)
+
+ .. testoutput::
+ :options: +MOCK
+
+ MaterializedDataset(
+ num_blocks=...,
+ num_rows=3257,
+ schema={text: string, label: int64}
+ )
 
  Args:
  dataset: A Hugging Face Dataset, or DatasetDict. IterableDataset is not