python 因子列表 python找因子

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mob64ca1411a6fc 2024-08-05 09:13:14

文章标签 python 因子列表 python因子分析法方差特征值因子分析 文章分类 Python 后端开发

因子分析用Python做的一个典型例子

一、实验目的

采用合适的数据分析方法对下面的题进行解答

python 因子列表 python找因子_方差

二、实验要求

采用因子分析方法，根据48位应聘者的15项指标得分，选出6名最优秀的应聘者。

三、代码

importpandas aspd
importnumpy asnp
importmath asmath
importnumpy asnp
fromnumpy import*
fromscipy.stats importbartlett
fromfactor_analyzer import*
importnumpy.linalg asnlg
fromsklearn.cluster importKMeans
frommatplotlib importcm
importmatplotlib.pyplot asplt
defmain():
df=pd.read_csv("./data/applicant.csv")
# print(df)df2=df.copy()
print("\n原始数据:\n",df2)
deldf2[‘ID‘]
# print(df2)#皮尔森相关系数df2_corr=df2.corr()
print("\n相关系数:\n",df2_corr)
#热力图cmap = cm.Blues
# cmap = cm.hot_rfig=plt.figure()
ax=fig.add_subplot(111)
map = ax.imshow(df2_corr, interpolation=‘nearest‘, cmap=cmap, vmin=0, vmax=1)
plt.title(‘correlation coefficient--headmap‘)
ax.set_yticks(range(len(df2_corr.columns)))
ax.set_yticklabels(df2_corr.columns)
ax.set_xticks(range(len(df2_corr)))
ax.set_xticklabels(df2_corr.columns)
plt.colorbar(map)
plt.show()
# KMO测度defkmo(dataset_corr):
corr_inv = np.linalg.inv(dataset_corr)
nrow_inv_corr, ncol_inv_corr = dataset_corr.shape
A = np.ones((nrow_inv_corr, ncol_inv_corr))
fori inrange(0, nrow_inv_corr, 1):
forj inrange(i, ncol_inv_corr, 1):
A[i, j] = -(corr_inv[i, j]) / (math.sqrt(corr_inv[i, i] * corr_inv[j, j]))
A[j, i] = A[i, j]
dataset_corr = np.asarray(dataset_corr)
kmo_num = np.sum(np.square(dataset_corr)) - np.sum(np.square(np.diagonal(A)))
kmo_denom = kmo_num + np.sum(np.square(A)) - np.sum(np.square(np.diagonal(A)))
kmo_value = kmo_num / kmo_denom
returnkmo_value
print("\nKMO测度:", kmo(df2_corr))
#巴特利特球形检验df2_corr1 = df2_corr.values
print("\n巴特利特球形检验:", bartlett(df2_corr1[0], df2_corr1[1], df2_corr1[2], df2_corr1[3], df2_corr1[4],
df2_corr1[5], df2_corr1[6], df2_corr1[7], df2_corr1[8], df2_corr1[9],
df2_corr1[10], df2_corr1[11], df2_corr1[12], df2_corr1[13], df2_corr1[14]))
#求特征值和特征向量eig_value, eigvector = nlg.eig(df2_corr) #求矩阵R的全部特征值，构成向量eig = pd.DataFrame()
eig[‘names‘] = df2_corr.columns
eig[‘eig_value‘] = eig_value
eig.sort_values(‘eig_value‘, ascending=False, inplace=True)
print("\n特征值\n：",eig)
eig1=pd.DataFrame(eigvector)
eig1.columns = df2_corr.columns
eig1.index = df2_corr.columns
print("\n特征向量\n",eig1)
#求公因子个数m,使用前m个特征值的比重大于85%的标准，选出了公共因子是五个form inrange(1, 15):
ifeig[‘eig_value‘][:m].sum() / eig[‘eig_value‘].sum() >= 0.85:
print("\n公因子个数:", m)
break#因子载荷阵A = np.mat(np.zeros((15, 5)))
i = 0
j = 0
whilei < 5:
j = 0
whilej < 15:
A[j:, i] = sqrt(eig_value[i]) * eigvector[j, i]
j = j + 1
i = i + 1
a = pd.DataFrame(A)
a.columns = [‘factor1‘, ‘factor2‘, ‘factor3‘, ‘factor4‘, ‘factor5‘]
a.index = df2_corr.columns
print("\n因子载荷阵\n", a)
fa = FactorAnalyzer(n_factors=5)
fa.loadings_ = a
# print(fa.loadings_)print("\n特殊因子方差:\n", fa.get_communalities()) #特殊因子方差，因子的方差贡献度 ，反映公共因子对变量的贡献var = fa.get_factor_variance() #给出贡献率print("\n解释的总方差（即贡献率）:\n", var)
#因子旋转rotator = Rotator()
b = pd.DataFrame(rotator.fit_transform(fa.loadings_))
b.columns = [‘factor1‘, ‘factor2‘, ‘factor3‘, ‘factor4‘, ‘factor5‘]
b.index = df2_corr.columns
print("\n因子旋转:\n", b)
#因子得分X1 = np.mat(df2_corr)
X1 = nlg.inv(X1)
b = np.mat(b)
factor_score = np.dot(X1, b)
factor_score = pd.DataFrame(factor_score)
factor_score.columns = [‘factor1‘, ‘factor2‘, ‘factor3‘, ‘factor4‘, ‘factor5‘]
factor_score.index = df2_corr.columns
print("\n因子得分：\n", factor_score)
fa_t_score = np.dot(np.mat(df2), np.mat(factor_score))
print("\n应试者的五个因子得分：\n",pd.DataFrame(fa_t_score))
#综合得分wei = [[0.50092], [0.137087], [0.097055], [0.079860], [0.049277]]
fa_t_score = np.dot(fa_t_score, wei) / 0.864198
fa_t_score = pd.DataFrame(fa_t_score)
fa_t_score.columns = [‘综合得分‘]
fa_t_score.insert(0, ‘ID‘, range(1, 49))
print("\n综合得分：\n", fa_t_score)
print("\n综合得分：\n", fa_t_score.sort_values(by=‘综合得分‘, ascending=False).head(6))
plt.figure()
ax1=plt.subplot(111)
X=fa_t_score[‘ID‘]
Y=fa_t_score[‘综合得分‘]
plt.bar(X,Y,color="#87CEFA")
# plt.bar(X, Y, color="red")plt.title(‘result00‘)
ax1.set_xticks(range(len(fa_t_score)))
ax1.set_xticklabels(fa_t_score.index)
plt.show()
fa_t_score1=pd.DataFrame()
fa_t_score1=fa_t_score.sort_values(by=‘综合得分‘,ascending=False).head()
ax2 = plt.subplot(111)
X1 = fa_t_score1[‘ID‘]
Y1 = fa_t_score1[‘综合得分‘]
plt.bar(X1, Y1, color="#87CEFA")
# plt.bar(X1, Y1, color=‘red‘)plt.title(‘result01‘)
plt.show()
if__name__ == ‘__main__‘:
main()

四、实验步骤

（1）引入数据，数据标准化

因为数据是面试中的得分，量纲相同，并且数据的分布无异常值，所以数据可以不进行标准化。

python 因子列表 python找因子_python因子分析法_02