ディープラーニングで、予測問題 家賃の予測
index:
学習データ
・目的変数
家賃
・説明変数
敷金、築年数 など。
コード
ニューロンの数
入力： 5(説明変数 の数)
隠れ層：10
出力層：1
import numpy as np import pandas as pd from pandas import Series, DataFrame from sklearn.model_selection import train_test_split from matplotlib import pyplot as plt from simple_net import SimpleNet from util_dt import * from util_df import * import time # # 学習データ # 学習データ global_start_time = time.time() wdata = pd.read_csv("data.csv" ) wdata.columns =["no", "price","siki_price", "rei_price" ,"menseki" ,"nensu" ,"toho" ,"madori" ,"houi" ,"kouzou" ] #print(wdata.head() ) #quit() # conv=> num sub_data = wdata[[ "no","price","siki_price", "rei_price" ,"menseki" ,"nensu" ,"toho" ] ] sub_data = sub_data.assign(price=pd.to_numeric( sub_data.price)) print( sub_data.head() ) print(sub_data["price"][: 10]) # 説明変数に "price" 以外を利用 X = sub_data.drop("price", axis=1) X = X.drop("no", axis=1) #num_max_x= 10 num_max_x= 1000 X = (X / num_max_x ) print(X.head() ) print(X.shape ) #print( type( X) ) #print(X[: 10 ] ) # 目的変数 num_max_y= num_max_x Y = sub_data["price"] Y = Y / num_max_y print(Y.max() ) print(Y.min() ) #quit() # 学習データとテストデータに分ける x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25 ,random_state=0) #x_train_sub =x_train #x_test_sub =x_test #x_train = x_train["x_dat"] #x_test = x_test["x_dat"] #print(type(x_train) ) #quit() x_train =np.array(x_train, dtype = np.float32).reshape(len(x_train), 5) y_train =np.array(y_train, dtype = np.float32).reshape(len(y_train), 1) x_test =np.array(x_test, dtype = np.float32).reshape(len(x_test), 5 ) y_test =np.array(y_test, dtype = np.float32).reshape(len(y_test), 1) # #x_train =np.array(x_train, dtype = np.float64 ).reshape(len(x_train), 5) #y_train =np.array(y_train, dtype = np.float64).reshape(len(y_train), 1) #x_test =np.array(x_test, dtype = np.float64).reshape(len(x_test), 5 ) #y_test =np.array(y_test, dtype = np.float64).reshape(len(y_test), 1) print( x_train.shape , y_train.shape ) print( x_test.shape , y_test.shape ) #print(x_train[: 10]) #print(type(x_train )) #quit() # network = SimpleNet(input_size=5 , hidden_size=10, output_size=1 ) #iters_num = 30000 # 繰り返しの回数を適宜設定する iters_num = 10000 # 繰り返しの回数を適宜設定する train_size = x_train.shape[0] print( train_size ) #quit() # global_start_time = time.time() #batch_size = 100 #batch_size = 32 batch_size = 16 learning_rate = 0.1 train_loss_list = [] train_acc_list = [] test_acc_list = [] # #iter_per_epoch =200 iter_per_epoch = 500 #print(iter_per_epoch) #quit() for i in range(iters_num): batch_mask = np.random.choice(train_size, batch_size) #print(batch_mask ) x_batch = x_train[batch_mask] t_batch = y_train[batch_mask] #quit()s # 勾配の計算 grad = network.gradient(x_batch, t_batch) # パラメータの更新 for key in ('W1', 'b1', 'W2', 'b2'): network.params[key] -= learning_rate * grad[key] loss = network.loss(x_batch, t_batch) train_loss_list.append(loss) if i % iter_per_epoch == 0: print ("i=" +str(i) + ', time : '+ str( time.time() - global_start_time) + " , loss=" +str(loss)) #print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc) + " , loss=" +str(loss) ) print ('time : ', time.time() - global_start_time) # # パラメータの保存 network.save_params("params.pkl") print("Saved Network Parameters!") #quit() #pred y_test_div=y_test[: 10] * num_max_y #print( y_test_div ) print( y_test_div ) y_val = network.predict(x_test[: 10]) y_val = y_val * num_max_y print( y_val )
評価
・予測した、先頭数件の家賃
・テストデータ家賃と、予測した家賃の比較
折れ線グラフ
