财政收入影响因素分析及预测

标签：因素 plt 预测 train 财政收入 np new data reg


import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 计算欧拉距离
def calcDis(dataSet, centroids, k):
    clalist=[]
    for data in dataSet:
        diff = np.tile(data, (k, 1)) - centroids  #相减   (np.tile(a,(2,1))就是把a先沿x轴复制1倍，即没有复制，仍然是 [0,1,2]。 再把结果沿y方向复制2倍得到array([[0,1,2],[0,1,2]]))
        squaredDiff = diff ** 2     #平方
        squaredDist = np.sum(squaredDiff, axis=1)   #和  (axis=1表示行)
        distance = squaredDist ** 0.5  #开根号
        clalist.append(distance) 
    clalist = np.array(clalist)  #返回一个每个点到质点的距离len(dateSet)*k的数组
    return clalist

# 计算质心
def classify(dataSet, centroids, k):
    # 计算样本到质心的距离
    clalist = calcDis(dataSet, centroids, k)
    # 分组并计算新的质心
    minDistIndices = np.argmin(clalist, axis=1)    #axis=1 表示求出每行的最小值的下标
    newCentroids = pd.DataFrame(dataSet).groupby(minDistIndices).mean() #DataFramte(dataSet)对DataSet分组，groupby(min)按照min进行统计分类，mean()对分类结果求均值
    newCentroids = newCentroids.values
 
    # 计算变化量
    changed = newCentroids - centroids
 
    return changed, newCentroids

# 使用k-means分类
def kmeans(dataSet, k):
    # 随机取质心
    centroids = random.sample(dataSet, k)
    
    # 更新质心 直到变化量全为0
    changed, newCentroids = classify(dataSet, centroids, k)
    while np.any(changed != 0):
        changed, newCentroids = classify(dataSet, newCentroids, k)
 
    centroids = sorted(newCentroids.tolist())   #tolist()将矩阵转换成列表 sorted()排序
 
    # 根据质心计算每个集群
    cluster = []
    clalist = calcDis(dataSet, centroids, k) #调用欧拉距离
    minDistIndices = np.argmin(clalist, axis=1)  
    for i in range(k):
        cluster.append([])
    for i, j in enumerate(minDistIndices):   #enymerate()可同时遍历索引和遍历元素
        cluster[j].append(dataSet[i])
        
    return centroids, cluster
 
# 创建数据集
def createDataSet():
    return [[1, 1], [1, 2], [2, 1], [6, 4], [6, 3], [5, 4]]

if __name__=='__main__': 
    dataset = createDataSet()
    centroids, cluster = kmeans(dataset, 2)
    print('质心为：%s' % centroids)
    print('集群为：%s' % cluster)
    for i in range(len(dataset)):
      plt.scatter(dataset[i][0],dataset[i][1], marker = 'o',color = 'green', s = 40 ,label = '原始点')
                                                    #  记号形状       颜色      点的大小      设置标签
      for j in range(len(centroids)):
        plt.scatter(centroids[j][0],centroids[j][1],marker='x',color='red',s=50,label='质心')
        plt.title('3126-mint')
        plt.show()

描述性统计分析

# 对各属性进行描述性统计分析
def statisticAnalysis():
    inputfile = '../data/data.csv'  # 输出的数据文件
    data = pd.read_csv(inputfile)  # 读数据
    # 最小值、最大值、均值、标准差
    description = [data.min(), data.max(), data.mean(), data.std()]

    # 将结果存入数据框
    description = pd.DataFrame(description, index=["Min", "Max", "Mean", "STD"]).T
    print("描述性统计结果：\n", np.round(description, 2))  # 保留两位

相关系数矩阵和热力图

# 求解原始数据的Pearson相关系数矩阵
def correlationCoefficientMatrix(data):
inputfile = '../data/data.csv' # 输出的数据文件
data = pd.read_csv(inputfile) # 读数据
corr = data.corr(method='pearson') # 计算相关系数矩阵
print("相关系数矩阵为：\n", np.round(corr, 2)) # 保留两位
return corr

# 绘制相关性热力图
def thermodynamic(corr):
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unicode_minus']=False
plt.subplots(figsize=(10, 10))
sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Blues_r")
plt.title("相关性热力图 3126")
plt.show()
plt.close()

构建模型并预测

# 构建灰色预测模型并预测
def grey():
    sys.path.append("D:/作业/数据挖掘/tmp")

    inputfile1 = "../data/new_reg_data.csv"
    inputfile2 = "../data/data.csv"
    new_reg_data = pd.read_csv(inputfile1)
    data = pd.read_csv(inputfile2)
    new_reg_data.index = range(1994, 2014)
    new_reg_data.loc[2014] = None
    new_reg_data.loc[2015] = None
    cols = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
    for i in cols:
        f = GM11(new_reg_data.loc[range(1994, 2014), i].values)[0]
        new_reg_data.loc[2014, i] = f(len(new_reg_data)-1)   # 2014年预测结果
        new_reg_data.loc[2015, i] = f(len(new_reg_data))  # 2015年预测结果
        new_reg_data[i] = new_reg_data[i].round(2)

    outputfile = '../tmp/new_reg_data_GM11.xls'            # 灰色预测后保存路径
    y = list(data['y'].values)
    y.extend([np.nan, np.nan])
    new_reg_data['y'] = y
    new_reg_data.to_excel(outputfile)
    print("预测结果为：\n",new_reg_data.loc[2014:2015,:])

# 构建支持向量回归预测模型
def SVR():
    from sklearn.svm import LinearSVR

    inputfile = '../tmp/new_reg_data_GM11.xls'
    data = pd.read_excel(inputfile)
    feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
    data.index = range(1994, 2016)
    data_train = data.loc[range(1994, 2014)].copy()
    data_mean = data_train.mean()
    data_std = data_train.std()
    data_train = (data_train - data_mean)/data_std
    x_train = data_train[feature].to_numpy()
    y_train = data_train['y'].to_numpy()

    linearsvr = LinearSVR()
    linearsvr.fit(x_train, y_train)
    x = ((data[feature] - data_mean[feature])/data_std[feature]).to_numpy()

    data[u'y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
    # outputfile = '../tmp/new_reg_data_GM11_revenue.xls'
    # data.to_excel(outputfile)

    print("真实值与预测值分别为：\n",data[['y', 'y_pred']])

    plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
    plt.rcParams['axes.unicode_minus'] = False

    fig = data[['y', 'y_pred']].plot(subplots = True,style=['b-o', 'r-*'])
    plt.title("3126")
    plt.show()

GM(1,1)

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

全部代码示意如下：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sys
from GM11 import GM11


inputfile = '../data/data.csv'  # 输出的数据文件
data = pd.read_csv(inputfile)  # 读数据


# 对各属性进行描述性统计分析
def statisticAnalysis(data):
    # 最小值、最大值、均值、标准差
    description = [data.min(), data.max(), data.mean(), data.std()]

    # 将结果存入数据框
    description = pd.DataFrame(description, index=["Min", "Max", "Mean", "STD"]).T
    print("描述性统计结果：\n", np.round(description, 2))  # 保留两位


# 求解原始数据的Pearson相关系数矩阵
def correlationCoefficientMatrix(data):
    corr = data.corr(method='pearson')  # 计算相关系数矩阵
    print("相关系数矩阵为：\n", np.round(corr, 2))  # 保留两位
    return corr


# 绘制相关性热力图
def thermodynamic(corr):
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
    plt.rcParams['axes.unicode_minus']=False
    plt.subplots(figsize=(10, 10))
    sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Blues_r")
    plt.title("相关性热力图 3126")
    plt.show()
    plt.close()


# 构建灰色预测模型并预测
def grey():
    sys.path.append("D:/作业/数据挖掘/tmp")

    inputfile1 = "../data/new_reg_data.csv"
    inputfile2 = "../data/data.csv"
    new_reg_data = pd.read_csv(inputfile1)
    data = pd.read_csv(inputfile2)
    new_reg_data.index = range(1994, 2014)
    new_reg_data.loc[2014] = None
    new_reg_data.loc[2015] = None
    cols = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
    for i in cols:
        f = GM11(new_reg_data.loc[range(1994, 2014), i].values)[0]
        new_reg_data.loc[2014, i] = f(len(new_reg_data)-1)   # 2014年预测结果
        new_reg_data.loc[2015, i] = f(len(new_reg_data))  # 2015年预测结果
        new_reg_data[i] = new_reg_data[i].round(2)

    outputfile = '../tmp/new_reg_data_GM11.xls'            # 灰色预测后保存路径
    y = list(data['y'].values)
    y.extend([np.nan, np.nan])
    new_reg_data['y'] = y
    new_reg_data.to_excel(outputfile)
    print("预测结果为：\n",new_reg_data.loc[2014:2015,:])

# 构建支持向量回归预测模型
def SVR():
    from sklearn.svm import LinearSVR

    inputfile = '../tmp/new_reg_data_GM11.xls'
    data = pd.read_excel(inputfile)
    feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
    data.index = range(1994, 2016)
    data_train = data.loc[range(1994, 2014)].copy()
    data_mean = data_train.mean()
    data_std = data_train.std()
    data_train = (data_train - data_mean)/data_std
    x_train = data_train[feature].to_numpy()
    y_train = data_train['y'].to_numpy()

    linearsvr = LinearSVR()
    linearsvr.fit(x_train, y_train)
    x = ((data[feature] - data_mean[feature])/data_std[feature]).to_numpy()

    data[u'y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
    # outputfile = '../tmp/new_reg_data_GM11_revenue.xls'
    # data.to_excel(outputfile)

    print("真实值与预测值分别为：\n",data[['y', 'y_pred']])

    plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
    plt.rcParams['axes.unicode_minus'] = False

    fig = data[['y', 'y_pred']].plot(subplots = True,style=['b-o', 'r-*'])
    plt.title("3126")
    plt.show()


statisticAnalysis(data)
corr = correlationCoefficientMatrix(data)
thermodynamic(corr)

grey()

SVR()

标签：因素,plt,预测,train,财政收入,np,new,data,reg
From： https://www.cnblogs.com/mintlyx/p/17182168.html

财政收入影响因素分析及预测

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