Upload covertype.py

covertype.py

@@ -19,59 +19,61 @@ urls_per_split = {
     "train": "https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz"
 }
 _BASE_FEATURE_NAMES = [
-    "elevation",
-    "aspect",
-    "slope",
-    "horizontal_distance_to_hydrology",
-    "vertical_distance_to_hydrology",
-    "horizontal_distance_to_roadways",
-    "hillshade_9am",
-    "hillshade_noon",
-    "hillshade_3pm",
-    "horizontal_distance_to_fire_points",
-    "is_a_wilderness_area",
-    "soil_type_id_0",
-    "soil_type_id_1",
-    "soil_type_id_2",
-    "soil_type_id_3",
-    "soil_type_id_4",
-    "soil_type_id_5",
-    "soil_type_id_6",
-    "soil_type_id_7",
-    "soil_type_id_8",
-    "soil_type_id_9",
-    "soil_type_id_10",
-    "soil_type_id_11",
-    "soil_type_id_12",
-    "soil_type_id_13",
-    "soil_type_id_14",
-    "soil_type_id_15",
-    "soil_type_id_16",
-    "soil_type_id_17",
-    "soil_type_id_18",
-    "soil_type_id_19",
-    "soil_type_id_20",
-    "soil_type_id_21",
-    "soil_type_id_22",
-    "soil_type_id_23",
-    "soil_type_id_24",
-    "soil_type_id_25",
-    "soil_type_id_26",
-    "soil_type_id_27",
-    "soil_type_id_28",
-    "soil_type_id_29",
-    "soil_type_id_30",
-    "soil_type_id_31",
-    "soil_type_id_32",
-    "soil_type_id_33",
-    "soil_type_id_34",
-    "soil_type_id_35",
-    "soil_type_id_36",
-    "soil_type_id_37",
-    "soil_type_id_38",
-    "soil_type_id_39",
-    "soil_type",
-    "cover_type"
+        "elevation",
+		"aspect",
+		"slope",
+		"horizontal_distance_to_hydrology",
+		"vertical_distance_to_hydrology",
+		"horizontal_distance_to_roadways",
+		"hillshade_9am",
+		"hillshade_noon",
+		"hillshade_3pm",
+		"horizontal_distance_to_fire_points",
+		"wilderness_area_id_0",
+        "wilderness_area_id_1",
+        "wilderness_area_id_2",
+        "wilderness_area_id_3",
+		"soil_type_id_0",
+		"soil_type_id_1",
+		"soil_type_id_2",
+		"soil_type_id_3",
+		"soil_type_id_4",
+		"soil_type_id_5",
+		"soil_type_id_6",
+		"soil_type_id_7",
+		"soil_type_id_8",
+		"soil_type_id_9",
+		"soil_type_id_10",
+		"soil_type_id_11",
+		"soil_type_id_12",
+		"soil_type_id_13",
+		"soil_type_id_14",
+		"soil_type_id_15",
+		"soil_type_id_16",
+		"soil_type_id_17",
+		"soil_type_id_18",
+		"soil_type_id_19",
+		"soil_type_id_20",
+		"soil_type_id_21",
+		"soil_type_id_22",
+		"soil_type_id_23",
+		"soil_type_id_24",
+		"soil_type_id_25",
+		"soil_type_id_26",
+		"soil_type_id_27",
+		"soil_type_id_28",
+		"soil_type_id_29",
+		"soil_type_id_30",
+		"soil_type_id_31",
+		"soil_type_id_32",
+		"soil_type_id_33",
+		"soil_type_id_34",
+		"soil_type_id_35",
+		"soil_type_id_36",
+		"soil_type_id_37",
+		"soil_type_id_38",
+		"soil_type_id_39",
+		"cover_type"
 ]
 features_types_per_config = {
     "covertype": {
@@ -85,7 +87,10 @@ features_types_per_config = {
 		"hillshade_noon": datasets.Value("float32"),
 		"hillshade_3pm": datasets.Value("float32"),
 		"horizontal_distance_to_fire_points": datasets.Value("float32"),
-		"is_a_wilderness_area": datasets.Value("bool"),
+        "wilderness_area_id_0": datasets.Value("bool"),
+        "wilderness_area_id_1": datasets.Value("bool"),
+        "wilderness_area_id_2": datasets.Value("bool"),
+        "wilderness_area_id_3": datasets.Value("bool"),
 		"soil_type_id_0": datasets.Value("bool"),
 		"soil_type_id_1": datasets.Value("bool"),
 		"soil_type_id_2": datasets.Value("bool"),
@@ -135,8 +140,11 @@ 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"]]
 
 
@@ -144,7 +152,7 @@ class Covertype(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "covertype"
     BUILDER_CONFIGS = [
-        CovertypeConfig(name="covertype",
+        CovertypeConfig(name="covertype"",
                     description="Covertype for multiclass classification.")
     ]
 
@@ -153,35 +161,49 @@ 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)
+            data = pandas.read_csv(filepath",
+		" header=None)
             print(data.columns)
-            print(data.shape[1], len(_BASE_FEATURE_NAMES))
+            print(data.shape[1]",
+		" len(_BASE_FEATURE_NAMES))
             data.columns = _BASE_FEATURE_NAMES
-            data = self.preprocess(data, config=self.config.name)
+            data = self.preprocess(data",
+		" config=self.config.name)
 
-            for row_id, row in data.iterrows():
+            for row_id",
+		" row in data.iterrows():
                 data_row = dict(row)
 
-                yield row_id, data_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)
+    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