Delete AutoPreprocess.py

AutoPreprocess.py

@@ -1,147 +0,0 @@
-import pandas as pd
-import numpy as np
-import pickle
-from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.preprocessing import LabelEncoder
-from sklearn.preprocessing import RobustScaler
-
-class AutoPreprocess(BaseEstimator, TransformerMixin):
-    def __init__(self):         
-        self.scaler = {}
-        self.fillna_value = {}
-        self.onehotencode_value = {}
-        self.field_names = []
-        self.final_field_names = []
-        self.field_dtype = {}
-        
-    def fit(self, X, y = None, field_names=None):
-        self.__init__()
-        if field_names is None:
-            self.field_names = X.columns.tolist()
-        else:
-            self.field_names = field_names       
-
-        for fname in self.field_names:
-            self.field_dtype = X[fname].dtype
-        
-        for fname in self.field_names:
-            #自動補空值
-            # if (X[fname].dtype == object) or (X[fname].dtype == str): #字串型態欄位
-            if pd.api.types.is_string_dtype(X[fname]):
-                self.fillna_value[fname] = X[fname].mode()[0] #補眾數
-                # self.fillna_value[fname] = 'np.nan'
-                # self.fillna_value[fname] = np.nan # 維持空值
-            # elif X[fname].dtype == bool: #布林型態
-            elif pd.api.types.is_bool_dtype(X[fname]):
-                self.fillna_value[fname] = X[fname].mode()[0] #補眾數
-            else: # 數字型態
-                self.fillna_value[fname] = X[fname].median()  #補中位數
-            
-            #自動尺度轉換(scaling)
-            # if (X[fname].dtype == object) or (X[fname].dtype == str): #字串型態欄位
-            if pd.api.types.is_string_dtype(X[fname]):
-                pass #不用轉換
-            # elif X[fname].dtype == bool: #布林型態
-            elif pd.api.types.is_bool_dtype(X[fname]):
-                pass #不用轉換
-            else: # 數字型態
-                vc = X[fname].value_counts()
-                if X[fname].isin([0, 1]).all(): #當數值只有0跟1
-                    pass #不用轉換
-                elif pd.api.types.is_integer_dtype(X[fname]) and X[fname].nunique() <= 10: #是否簡單的整數型類別且數量小於10
-                    self.scaler[fname] = MinMaxScaler()    
-                    self.scaler[fname].fit(X[[fname]])
-                else: #其他的數字型態
-                    self.scaler[fname] = RobustScaler()    
-                    self.scaler[fname].fit(X[[fname]])
-
-            
-            #自動編碼
-            # if (X[fname].dtype == object) or (X[fname].dtype == str): #字串型態欄位
-            if pd.api.types.is_string_dtype(X[fname]):
-                field_value = X[fname].value_counts().index
-                self.onehotencode_value[fname] = field_value
-                for value in field_value:
-                    fn = fname+"_"+value
-                    # data[fn] = (data[fname] == value).astype('int8')
-                    self.final_field_names.append(fn)                    
-            # elif X[fname].dtype == bool: #布林型態
-            elif pd.api.types.is_bool_dtype(X[fname]):
-                # data[fname] = data[fname].astype(int)
-                self.final_field_names.append(fname)
-            else: # 數字型態 不用重新編碼
-                self.final_field_names.append(fname)
-                
-        return self
-
-    def transform(self, X):
-        #如果輸入的data是dict，要先轉成dataframe
-        if isinstance(X, dict):
-            for fname in self.field_names:
-                if fname in X:
-                    X[fname] = [X[fname]]
-                else:
-                    # X[fname] = [np.nan]
-                    X[fname] = self.fillna_value[fname]
-                
-            data = pd.DataFrame(X)
-
-            # for fname in self.field_names:
-                # data[fname].astype(self.field_dtype[fname])
-
-        else: #將資料複製一份，不修改原本的資料
-            data = X.copy()
-        
-        for fname in self.field_names:
-            #自動補空值
-            if data[fname].isnull().any(): #有空值
-                # if fname in self.fillna_value:
-                    data[fname] = data[fname].fillna(self.fillna_value[fname])
-
-
-            #自動尺度轉換(scaling)
-            if fname in self.scaler:
-                data[fname] = self.scaler[fname].transform(data[[fname]])
-            
-            #自動編碼
-            # if (data[fname].dtype == object) or (data[fname].dtype == str): #字串型態欄位, onehotencode
-            if pd.api.types.is_string_dtype(data[fname]):
-                if fname in self.onehotencode_value:                   
-                    field_value = self.onehotencode_value[fname]
-                for value in field_value:
-                    fn = fname+"_"+value
-                    data[fn] = (data[fname] == value).astype('int8')
-            # elif data[fname].dtype == bool: #布林型態 轉成0跟1
-            elif pd.api.types.is_bool_dtype(data[fname]):
-                data[fname] = data[fname].astype(int)
-            else: # 數字型態 不用重新編碼
-                pass                
-        return data[self.final_field_names]
-
-    def save(self, file_name):
-        with open(file_name, "wb") as f:
-            pickle.dump(self, f)
-
-    @staticmethod
-    def load(file_name):
-        with open(file_name, "rb") as f:
-            return pickle.load(f)          
-        
-
-# import pandas as pd
-# mydata = pd.read_csv('C:/DATA/class/2025-07 AI數據應用人才養成班三期/data/Automobile_Train.csv')
-# ap = AutoPreprocess()
-# # ap.fit(mydata, field_names=['symboling', 'Normalized-losses', 'make', 'Fuel-type', 'aspiration',
-# #        'Num-of-doors', 'Body-style', 'Drive-wheels', 'Engine-location',
-# #        'Wheel-base', 'length', 'width', 'height', 'Curb-weight', 'Engine-type',
-# #        'Num-of-cylinders', 'Engine-size', 'Fuel-system', 'bore', 'stroke',
-# #        'Compression-ratio', 'horsepower', 'Peak-rpm', 'City-mpg',
-# #        'Highway-mpg'])
-# ap.fit(mydata)
-
-# # 轉換 panddas dataframe
-# t = ap.transform(mydata)
-# print(t.head())
-
-