Upload nbfi.py

nbfi.py

@@ -145,8 +145,7 @@ _CITATION = """"""
 
 # Dataset info
 urls_per_split = {
-    "train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv", 
-    "test": "https://gist.githubusercontent.com/msetzu/f0032b855008f579299d7ad78d9dd9c2/raw/ba42badeb10b505cb283bdb16d3de581ffe7a332/Test_Dataset.csv"
+    "train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv"
 }
 
 features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
@@ -180,8 +179,7 @@ class NBFI(datasets.GeneratorBasedBuilder):
         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.TEST, gen_kwargs={"filepath": downloads["test"]}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
         ]
     
     def _generate_examples(self, filepath: str):
@@ -199,6 +197,7 @@ class NBFI(datasets.GeneratorBasedBuilder):
 
 
     def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        print(data.columns)
         data.drop("ID", axis="columns", inplace=True)
         data.drop("Own_House_Age", axis="columns", inplace=True)
         data.drop("Type_Organization", axis="columns", inplace=True)
@@ -237,16 +236,6 @@ class NBFI(datasets.GeneratorBasedBuilder):
         data = data[data.Registration_Days != "x"]
         data = data[data.ID_Days != "x"]
         
-        print("len(data.columns)")
-        print(len(data.columns))
-        print("len(_BASE_FEATURE_NAMES)")
-        print(len(_BASE_FEATURE_NAMES))
-        print("len(features_types_per_config[config].keys())")
-        print(len(features_types_per_config[config].keys()))
-        print("data.columns, features_types_per_config, _BASE_FEATURE_NAMES")
-        for f, ft, fb in zip(data.columns, features_types_per_config[config].keys(), _BASE_FEATURE_NAMES):
-            print(f, ft, fb)
-
         data.columns = _BASE_FEATURE_NAMES
 
         data["education_level"] = data["education_level"].apply(lambda x: _EDUCATION_ENCODING[x])