Upload covertype.py

covertype.py

@@ -140,11 +140,8 @@ features_per_config = {k: datasets.Features(features_types_per_config[k]) for k
 
 
 class CovertypeConfig(datasets.BuilderConfig):
-    def __init__(self",
-		" **kwargs):
-        super(CovertypeConfig",
-		" self).__init__(version=VERSION",
-		" **kwargs)
+    def __init__(self, **kwargs):
+        super(CovertypeConfig, self).__init__(version=VERSION, **kwargs)
         self.features = features_per_config[kwargs["name"]]
 
 
@@ -152,7 +149,7 @@ class Covertype(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "covertype"
     BUILDER_CONFIGS = [
-        CovertypeConfig(name="covertype"",
+        CovertypeConfig(name="covertype",
                     description="Covertype for multiclass classification.")
     ]
 
@@ -161,49 +158,32 @@ class Covertype(datasets.GeneratorBasedBuilder):
         if self.config.name not in features_per_config:
             raise ValueError(f"Unknown configuration: {self.config.name}")
         
-        info = datasets.DatasetInfo(description=DESCRIPTION",
-		" citation=_CITATION",
-		" homepage=_HOMEPAGE",
+        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                     features=features_per_config[self.config.name])
 
         return info
     
-    def _split_generators(self",
-		" dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         downloads = dl_manager.download_and_extract(urls_per_split)
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN",
-		" gen_kwargs={"filepath": downloads["train"]})
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
         ]
     
-    def _generate_examples(self",
-		" filepath: str):
+    def _generate_examples(self, filepath: str):
             # try:
             #     with gzip.open(filepath) as log:
-            #         data = pandas.read_csv(log",
-		" header=None)
+            #         data = pandas.read_csv(log", header=None)
             # except gzip.BadGzipFile:
-            data = pandas.read_csv(filepath",
-		" header=None)
-            print(data.columns)
-            print(data.shape[1]",
-		" len(_BASE_FEATURE_NAMES))
-            data.columns = _BASE_FEATURE_NAMES
-            data = self.preprocess(data",
-		" config=self.config.name)
-
-            for row_id",
-		" row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id",
-		" data_row
-
-    def preprocess(self",
-		" data: pandas.DataFrame",
-		" config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.loc[:",
-		" "cover_type"] = data["cover_type"].apply(lambda x: x - 1)
+        data = pandas.read_csv(filepath, header=None)
+        data.columns = _BASE_FEATURE_NAMES
+        data = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+            yield row_id, data_row
+
+    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data.loc[:, "cover_type"] = data["cover_type"].apply(lambda x: x - 1)
         
         return data