数据分析第六章
财政收入印象因素及预测
1、对相关数据进行描述性统计
import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.rcParams ['font.sans-serif'] ='SimHei' #显示中文 plt.rcParams ['axes.unicode_minus']=False #显示负号 inputfile = r"D:\py_project\a_三下\data.csv" data = pd.read_csv(inputfile) # 描述性统计 description = [data.min(),data.max(),data.mean(),data.std()] description = pd.DataFrame(description, index = ['Min','Max','Mean','STD']).T print(np.round(description,2))
2、相关系数热力图
# 相关系数 corr = data.corr(method = 'pearson') print('\n',np.round(corr,2)) import matplotlib.pyplot as plt import seaborn as sns plt.subplots(figsize=(10, 10)) # 设置画面大小 sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Blues") plt.title('相关性热力图 3141') plt.show() plt.close
3、lasso回归选取关键属性
# lasso回归选取关键属性 from sklearn.linear_model import Lasso lasso = Lasso(1000) lasso.fit(data.iloc[:,0:13],data['y']) print(np.round(lasso.coef_,5)) print(np.sum(lasso.coef_ != 0)) mask = lasso.coef_!=0 print('相关系数是否为零:',mask) mask = np.append(mask,True) outputfile=r'D:\py_project\a_三下\new_reg_data.csv' new_reg_data=data.iloc[:,mask] new_reg_data.to_csv(outputfile) print('输出数据的维度为:',new_reg_data.shape)
4、灰色预测
def GM11(x0): #自定义灰色预测函数 import numpy as np x1 = x0.cumsum() #1-AGO序列 z1 = (x1[:len(x1)-1] + x1[1:])/2.0 #紧邻均值(MEAN)生成序列 z1 = z1.reshape((len(z1),1)) B = np.append(-z1, np.ones_like(z1), axis = 1) Yn = x0[1:].reshape((len(x0)-1, 1)) [[a],[b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn) #计算参数 f = lambda k: (x0[0]-b/a)*np.exp(-a*(k-1))-(x0[0]-b/a)*np.exp(-a*(k-2)) #还原值 delta = np.abs(x0 - np.array([f(i) for i in range(1,len(x0)+1)])) C = delta.std()/x0.std() P = 1.0*(np.abs(delta - delta.mean()) < 0.6745*x0.std()).sum()/len(x0) return f, a, b, x0[0], C, P #返回灰色预测函数、a、b、首项、方差比、小残差概率 inputfile1 = r'D:/py_project/a_三下/new_reg_data.csv' # 输入的数据文件 inputfile2 = r'D:/py_project/a_三下/data.csv' # 输入的数据文件 new_reg_data = pd.read_csv(inputfile1) # 读取经过特征选择后的数据 data = pd.read_csv(inputfile2) # 读取总的数据 new_reg_data.index = range(1994, 2014) new_reg_data.loc[2014] = None new_reg_data.loc[2015] = None new_reg_data.loc[2016] = None l = ['x1', 'x4', 'x5', 'x6', 'x7', 'x8'] for i in l: f = GM11(new_reg_data.loc[range(1994, 2014),i].to_numpy())[0] new_reg_data.loc[2014,i] = f(len(new_reg_data)-1) # 2014年预测结果 print(new_reg_data.loc[2014,i]) new_reg_data.loc[2015,i] = f(len(new_reg_data)) # 2015年预测结果 print(new_reg_data.loc[2015,i]) new_reg_data.loc[2016,i] = f(len(new_reg_data)+1) # 2016年预测结果 print(new_reg_data.loc[2016,i]) new_reg_data[i] = new_reg_data[i].round(3) # 保留两位小数 print("*"*50) outputfile = './new_reg_data_GM11.xls' # 灰色预测后保存的路径 y = list(data['y'].values) # 提取财政收入列,合并至新数据框中 y.extend([np.nan,np.nan,np.nan]) new_reg_data['y'] = y new_reg_data.to_excel(outputfile) # 结果输出 print('预测结果为:\n',new_reg_data.loc[2015:2016,:]) # 预测结果展示