배치 경사 하강법 using algorithm (boston)

1. 데이터 불러오기

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston

boston = load_boston()
boston_df = pd.DataFrame(boston.data, columns = boston["feature_names"])
boston_df["PRICE"] = boston.target
boston_df.head()

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2. 배치 경사 하강법, 함수로 정의

def gradient(feature, target, learning_rate, iter_epochs, verbose):
    
    bias = np.random.rand(1,)
    w1   = np.random.rand(1,)
    w2   = np.random.rand(1,)
    
    feature_1 = feature[:, 0]
    feature_2 = feature[:, 1]
   
    
    print("####### 최초 bias, w1, w2 #######\n")
    print("bias : {}\tw1 : {}\tw2 : {}\n\n\n".format(bias, w1, w2))
    
    for i in range(iter_epochs) :
        
        N = len(target)
        
        predict = bias + w1 * feature_1 + w2 * feature_2
        diff    = target - predict
        
        bias_update = -(2/N) * learning_rate * (np.dot(np.ones((N,)), diff))
        w1_update   = -(2/N) * learning_rate * (np.dot(feature_1.T, diff))
        w2_update   = -(2/N) * learning_rate * (np.dot(feature_2.T, diff))
        
        err = np.mean(np.square(diff))
        
        bias = bias - bias_update
        w1   = w1 - w1_update
        w2   = w2 - w2_update
        
        if verbose == True :
            
            print("Epoch(   {}/{})\tbias : {}\tw1 : {}\tw2 : {}\terror : {}"
                                               .format(i+1, iter_epochs, bias, w1, w2, err))
              
    return bias, w1, w2

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3. feature data 전처리 과정

from sklearn.preprocessing import MinMaxScaler

mms = MinMaxScaler()
scaled_feature = mms.fit_transform(boston_df[["RM", "LSTAT"]])

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4. 배치 경사 하강법을 통한 가중치, 절편 확인

bias, w1, w2 = gradient(scaled_feature, boston_df["PRICE"], learning_rate = 0.01, iter_epochs = 1000, 
                                                                                verbose = True)

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5. 도출해낸 가중치, 절편으로 선형식을 완성하고, 예측 진행

z = bias + w1 * scaled_feature[:, 0] + w2 * scaled_feature[:, 1]

print(z)

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6. 예측 결과를 DataFrame에 추가

boston_df["PREDICT_using_hand"] = z

boston_df.head()