Add features parameter to CSV

datasets/csv/csv.py

@@ -20,6 +20,7 @@ class CsvConfig(datasets.BuilderConfig):
     read_options: pac.ReadOptions = None
     parse_options: pac.ParseOptions = None
     convert_options: pac.ConvertOptions = None
+    features: datasets.Features = None
 
     @property
     def pa_read_options(self):
@@ -43,7 +44,9 @@ def pa_parse_options(self):
 
     @property
     def pa_convert_options(self):
-        convert_options = self.convert_options or pac.ConvertOptions()
+        convert_options = self.convert_options or pac.ConvertOptions(
+            column_types=self.features.type if self.features is not None else None
+        )
         return convert_options
 
 
@@ -78,6 +81,6 @@ def _generate_tables(self, files):
                 file,
                 read_options=self.config.pa_read_options,
                 parse_options=self.config.pa_parse_options,
-                convert_options=self.config.convert_options,
+                convert_options=self.config.pa_convert_options,
             )
             yield i, pa_table

datasets/text/text.py

@@ -104,7 +104,7 @@ def _generate_tables(self, files):
                 file,
                 read_options=self.config.pa_read_options,
                 parse_options=self.config.pa_parse_options,
-                convert_options=self.config.convert_options,
+                convert_options=self.config.pa_convert_options,
             )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")

src/datasets/builder.py

@@ -25,14 +25,14 @@
 from functools import partial
 from typing import Dict, List, Optional, Union
 
-import xxhash
 from filelock import FileLock
 
 from . import utils
 from .arrow_dataset import Dataset
 from .arrow_reader import HF_GCP_BASE_URL, ArrowReader, DatasetNotOnHfGcs, MissingFilesOnHfGcs
 from .arrow_writer import ArrowWriter, BeamWriter
 from .dataset_dict import DatasetDict
+from .fingerprint import Hasher
 from .info import (
     DATASET_INFO_FILENAME,
     DATASET_INFOS_DICT_FILE_NAME,
@@ -153,6 +153,8 @@ def __init__(
 
         # Prepare config: DatasetConfig contains name, version and description but can be extended by each dataset
         config_kwargs = dict((key, value) for key, value in config_kwargs.items() if value is not None)
+        if "features" in inspect.signature(self.BUILDER_CONFIG_CLASS.__init__).parameters and features is not None:
+            config_kwargs["features"] = features
         self.config = self._create_builder_config(
             name,
             **config_kwargs,
@@ -256,7 +258,7 @@ def _create_builder_config(self, name=None, **config_kwargs):
             # if not builder_config.description:
             #     raise ValueError("BuilderConfig %s must have a description" % name)
         if builder_config.data_files is not None:
-            m = xxhash.xxh64()
+            m = Hasher()
             if isinstance(builder_config.data_files, str):
                 data_files = {"train": [builder_config.data_files]}
             elif isinstance(builder_config.data_files, (tuple, list)):
@@ -269,10 +271,12 @@ def _create_builder_config(self, name=None, **config_kwargs):
             else:
                 raise ValueError("Please provide a valid `data_files` in `DatasetBuilder`")
             for key in sorted(data_files.keys()):
-                m.update(key.encode("utf-8"))
+                m.update(key)
                 for data_file in data_files[key]:
-                    m.update(os.path.abspath(data_file).encode("utf-8"))
-                    m.update(str(os.path.getmtime(data_file)).encode("utf-8"))
+                    m.update(os.path.abspath(data_file))
+                    m.update(str(os.path.getmtime(data_file)))
+            if hasattr(builder_config, "features"):
+                m.update(builder_config.features)
             builder_config.name += "-" + m.hexdigest()
         return builder_config
 

tests/test_dataset_common.py

@@ -28,8 +28,10 @@
     BuilderConfig,
     DatasetBuilder,
     DownloadConfig,
+    Features,
     GenerateMode,
     MockDownloadManager,
+    Value,
     cached_path,
     hf_api,
     hf_bucket_url,
@@ -375,13 +377,17 @@ def test_load_real_dataset_all_configs(self, dataset_name):
 
 class TextTest(TestCase):
     def test_caching(self):
+        n_samples = 10
         with tempfile.TemporaryDirectory() as tmp_dir:
-            open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write("\n".join("foo" for _ in range(10)))
+            open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write(
+                "\n".join("foo" for _ in range(n_samples))
+            )
             ds = load_dataset(
                 "./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
             )
             data_file = ds._data_files[0]
             fingerprint = ds._fingerprint
+            self.assertEqual(len(ds), n_samples)
             del ds
             ds = load_dataset(
                 "./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
@@ -390,10 +396,81 @@ def test_caching(self):
             self.assertEqual(ds._fingerprint, fingerprint)
             del ds
 
-            open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write("\n".join("bar" for _ in range(10)))
+            open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write(
+                "\n".join("bar" for _ in range(n_samples))
+            )
             ds = load_dataset(
                 "./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
             )
             self.assertNotEqual(ds._data_files[0], data_file)
             self.assertNotEqual(ds._fingerprint, fingerprint)
             del ds
+
+
+class CsvTest(TestCase):
+    def test_caching(self):
+        n_rows = 10
+
+        features = Features({"foo": Value("string"), "bar": Value("string")})
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
+                "\n".join(",".join(["foo", "bar"]) for _ in range(n_rows + 1))
+            )
+            ds = load_dataset(
+                "./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
+            )
+            data_file = ds._data_files[0]
+            fingerprint = ds._fingerprint
+            self.assertEqual(len(ds), n_rows)
+            del ds
+            ds = load_dataset(
+                "./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
+            )
+            self.assertEqual(ds._data_files[0], data_file)
+            self.assertEqual(ds._fingerprint, fingerprint)
+            del ds
+            ds = load_dataset(
+                "./datasets/csv",
+                data_files=os.path.join(tmp_dir, "table.csv"),
+                cache_dir=tmp_dir,
+                split="train",
+                features=features,
+            )
+            self.assertNotEqual(ds._data_files[0], data_file)
+            self.assertNotEqual(ds._fingerprint, fingerprint)
+            del ds
+
+            open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
+                "\n".join(",".join(["Foo", "Bar"]) for _ in range(n_rows + 1))
+            )
+            ds = load_dataset(
+                "./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
+            )
+            self.assertNotEqual(ds._data_files[0], data_file)
+            self.assertNotEqual(ds._fingerprint, fingerprint)
+            del ds
+
+    def test_features(self):
+        n_rows = 10
+        n_cols = 3
+
+        def get_features(type):
+            return Features({str(i): Value(type) for i in range(n_cols)})
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
+                "\n".join(",".join([str(i) for i in range(n_cols)]) for _ in range(n_rows + 1))
+            )
+            for type in ["float64", "int8"]:
+                features = get_features(type)
+                ds = load_dataset(
+                    "./datasets/csv",
+                    data_files=os.path.join(tmp_dir, "table.csv"),
+                    cache_dir=tmp_dir,
+                    split="train",
+                    features=features,
+                )
+                self.assertEqual(len(ds), n_rows)
+                self.assertDictEqual(ds.features, features)
+                del ds