Upload bank.py

bank.py

@@ -1,6 +1,7 @@
 """Bank Dataset"""
 
 from typing import List
+from functools import partial
 
 import datasets
 
@@ -31,7 +32,7 @@ _BASE_FEATURE_NAMES = [
     "age",
     "job",
     "marital_status",
-    "education",
+    "education_level",
     "has_defaulted",
     "account_balance",
     "has_housing_loan",
@@ -42,6 +43,31 @@ _BASE_FEATURE_NAMES = [
     "number_of_calls_before_this_campaign",
     "successfull_subscription"
 ]
+_ENCODING_DICS = {
+    "education_level": {
+        "unknown": 0,
+        "primary": 1,
+        "secondary": 2,
+        "tertiary": 3
+    },
+    "has_personal_loan": {
+        "no": 0,
+        "yes": 1
+    },
+    "has_housing_loan": {
+        "no": 0,
+        "yes": 1
+    },
+    "has_defaulted": {
+        "no": 0,
+        "yes": 1
+    },
+    "successful_subscription": {
+        "no": 0,
+        "yes": 1
+    }
+}
+
 
 DESCRIPTION = "Bank dataset for subscription prediction."
 _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/bank+marketing"
@@ -124,77 +150,33 @@ class Bank(datasets.GeneratorBasedBuilder):
 
             yield row_id, data_row
 
-    def preprocess(self, data: pandas.DataFrame, config: str = "income") -> pandas.DataFrame:
-        data.drop("day", axis="columns", inplace=True)
-        data.drop("contact", axis="columns", inplace=True)
-        data.drop("duration", axis="columns", inplace=True)
-        data.drop("poutcome", axis="columns", inplace=True)
-
-        # discretize features
-        data.loc[:, "education"] = data.education.apply(self.encode_education)
-        data.loc[:, "loan"] = data.loan.apply(self.encode_yes_no)
-        data.loc[:, "housing"] = data.housing.apply(self.encode_yes_no)
-        data.loc[:, "default"] = data.default.apply(self.encode_yes_no)
-        
-        data.columns = _BASE_FEATURE_NAMES
-        
-        data.loc[:, "successfull_subscription"] = data.successfull_subscription.apply(lambda x: 0 if x == "no" else 1)
+    def preprocess(self, data: pandas.DataFrame, config: str = "subscription") -> pandas.DataFrame:
+        if config == "subscription":
+            data.drop("day", axis="columns", inplace=True)
+            data.drop("contact", axis="columns", inplace=True)
+            data.drop("duration", axis="columns", inplace=True)
+            data.drop("poutcome", axis="columns", inplace=True)
 
-        for f in _BASE_FEATURE_NAMES:
-            print(f, data[f].max(), data.dtypes[f])
+            data.columns = _BASE_FEATURE_NAMES
+
+            for feature in _ENCODING_DICS:
+                encoding_function = partial(self.encode, feature)
+                data.loc[:, feature] = data[feature].apply(encoding_function)
 
-        if config == "subscription":
             return data
         else:
             raise ValueError(f"Unknown config: {config}")
+       
+    def encode(self, feature, value):
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")
+
+    def encoding_dics(self):
+        data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
+                for feature, d in _ENCODING_DICS.items()]
+        data = pandas.concat(data, axis="rows").reset_index()
+        data.drop("index", axis="columns", inplace=True)
+        data.columns = ["feature", "original_value", "encoded_value"]
 
-    def encoding_dictionaries(self):
-        education_dic, yes_no_dic = self.education_encoding_dic(), self.yes_no_encoding_dic()
-        education_data = [("education", education, code) for education, code in education_dic.items()]
-        loan_data = [("loan", loan, code) for loan, code in yes_no_dic.items()]
-        housing_data = [("housing", housing, code) for housing, code in yes_no_dic.items()]
-        default_data = [("default", default, code) for default, code in yes_no_dic.items()]
-        data = pandas.DataFrame(education_data + loan_data + housing_data + default_data,
-                                columns=["feature", "original_value", "encoded_value"])
-               
         return data
-
-    def encode_education(self, education):
-        return self.education_encoding_dic()[education]
-
-    def decode_education(self, code):
-        return self.education_decoding_dic()[code]
-    
-    def education_decoding_dic(self):
-        return {
-            0: "unknown",
-            1: "primary",
-            2: "secondary",
-            3: "tertiary"
-        }
-
-    def education_encoding_dic(self):
-        return {
-            "unknown": 0,
-            "primary": 1,
-            "secondary": 2,
-            "tertiary": 3
-        }
-        
-    def encode_yes_no(self, yes_no):
-        return self.yes_no_encoding_dic()[yes_no]
-
-    def decode_yes_no(self, code):
-        return self.yes_no_decoding_dic()[code]
-    
-    def yes_no_decoding_dic(self):
-        return {
-            0: "no",
-            1: "yes"
-        }
-
-    def yes_no_encoding_dic(self):
-        return {
-            "no": 0,
-            "yes": 1
-        }