Question

来自https://github.com/llSourcell/predicting_stock_prices/blob/master/demo.py的代码当我在jupyter笔记本中运行它时会挂起并挂在最后一行。我在笔记本和下载文件夹中有.csv ...不确定是否是错误

import csv
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt


#plt.switch_backend('newbackend')  

dates = []
prices = []

def get_data(filename):
    with open(filename, 'r') as csvfile:
        csvFileReader = csv.reader(csvfile)
        next(csvFileReader) # skipping column names
        for row in csvFileReader:
            dates.append(int(row[0].split('-')[0]))
            prices.append(float(row[1]))
    return

def predict_price(dates, prices, x):
    dates = np.reshape(dates,(len(dates), 1)) # converting to matrix of n X 1

    svr_lin = SVR(kernel= 'linear', C= 1e3)
    svr_poly = SVR(kernel= 'poly', C= 1e3, degree= 2)
    svr_rbf = SVR(kernel= 'rbf', C= 1e3, gamma= 0.1) # defining the support vector regression models
    svr_rbf.fit(dates, prices) # fitting the data points in the models
    svr_lin.fit(dates, prices)
    svr_poly.fit(dates, prices)

    plt.scatter(dates, prices, color= 'black', label= 'Data') # plotting the initial datapoints 
    plt.plot(dates, svr_rbf.predict(dates), color= 'red', label= 'RBF model') # plotting the line made by the RBF kernel
    plt.plot(dates,svr_lin.predict(dates), color= 'green', label= 'Linear model') # plotting the line made by linear kernel
    plt.plot(dates,svr_poly.predict(dates), color= 'blue', label= 'Polynomial model') # plotting the line made by polynomial kernel
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.title('Support Vector Regression')
    plt.legend()
    plt.show()

    return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]

get_data('table.csv') # calling get_data method by passing the csv file to it

predicted_price = predict_price(dates, prices, 29)

我已将代码划分为jupyter中的单元格，predicted_price似乎挂起 In [*]:

Answer 1

代码很好。 SVR需要时间来计算。了解更多here。您可以使用线性回归尝试以下代码。

带导入

from sklearn import linear_model

# defining the linear regression model
linear_mod = linear_model.LinearRegression()  

# fitting the data points in the model
linear_mod.fit(dates, prices)  

plt.scatter(dates, prices, color='black', label='Data') 
# plotting the initial datapoints 
plt.plot(dates, linear_mod.predict(dates), color='red',
             label='Linear model')

Jupyter笔记本挂着sklearn回归？

1 个答案: