[사이킷런] 램가격 모델링

from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import mglearn
 
class RamPrice:
    def time_series_chart(self):
        ram_price = pd.read_csv(os.path.join(mglearn.datasets.DATA_PATH, "ram_price.csv"))
        plt.semilogy(ram_price.date, ram_price.price)
        plt.xlabel('년')
        plt.ylabel('가격')
        # plt.show()
        data_train = ram_price[ram_price['date'] < 2000]
        data_test = ram_price[ram_price['date'] >= 2000]
        x_train = data_train['date'][:,np.newaxis] # train data 를 1 열로 만든다
        y_train = np.log(data_train['price'])
        regressor = DecisionTreeRegressor()
        tree = regressor.fit(x_train, y_train)
        lr = LinearRegression().fit(x_train, y_train)
        # test 는 모든 데이터 (1960 ~ 2010)에 대해 적용한다
        x_all = ram_price['date'].values.reshape(-1, 1) # x_all 을 1열로 만든다
        pred_tree = tree.predict(x_all)
        price_tree = np.exp(pred_tree) # log 값 되돌리기
        pred_lr = lr.predict(x_all)
        price_lr = np.exp(pred_lr) # log 값 되돌리기
 
        plt.semilogy(ram_price['date'], pred_tree, label='Tree Predic', ls = '-', dashes=(2,1))
        plt.semilogy(ram_price['date'], pred_lr, label='Linear Predic', ls = ':')
        plt.semilogy(data_train['date'], data_train['price'], label='Train Data', alpha=0.4)
        plt.semilogy(data_test['date'], data_test['price'], label='Test Data')
        plt.legend(loc = 1)
        plt.xlabel('year', size=15)
        plt.ylabel('price', size=15)
        plt.show()
 
if __name__ == '__main__':
    this = RamPrice()
    this.time_series_chart()