无法从sklearn PCA中提取因子负荷

Question

I want factor loadings to see which factor loads to which variables. I am referring to following link:

Factor Loadings using sklearn

Here is my code where input_data is the master_data.

X=master_data_predictors.values

#Scaling the values
X = scale(X)

#taking equal number of components as equal to number of variables
#intially we have 9 variables
pca = PCA(n_components=9)

pca.fit(X)

#The amount of variance that each PC explains
var= pca.explained_variance_ratio_

#Cumulative Variance explains
var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)

print var1
[ 74.75  85.85  94.1   97.8   98.87  99.4   99.75 100.   100.  ]

#Retaining 4 components as they explain 98% of variance
pca = PCA(n_components=4)
pca.fit(X)
X1=pca.fit_transform(X)

print pca.components_

array([[ 0.38454129,  0.37344315,  0.2640267 ,  0.36079567,  0.38070046,
         0.37690887,  0.32949014,  0.34213449,  0.01310333],
       [ 0.00308052,  0.00762985, -0.00556496, -0.00185015,  0.00300425,
         0.00169865,  0.01380971,  0.0142307 , -0.99974635],
       [ 0.0136128 ,  0.04651786,  0.76405944,  0.10212738,  0.04236969,
         0.05690046, -0.47599931, -0.41419841, -0.01629199],
       [-0.09045103, -0.27641087,  0.53709146, -0.55429524,  0.058524  ,
        -0.19038107,  0.4397584 ,  0.29430344,  0.00576399]])

import math
loadings = pca.components_.T * math.sqrt(pca.explained_variance_)

It gives me following error 'only length-1 arrays can be converted to Python scalars

I understand the problem. I have to traverse the pca.components_ and pca.explained_variance_ arrays such as:

##just a thought
Loading=np.empty((8,4))

for i,j in (pca.components_, pca.explained_variance_):
    loading=i*math.sqrt(j)
    Loading=Loading.append(loading)
##unable to proceed further 
##something wrong here   

Answer 1

This is simply a problem of mixing modules. For numpy arrays, use np.sqrt instead of math.sqrt (which only works on single values, not arrays).

Your last line should thus read:

loadings = pca.components_.T * np.sqrt(pca.explained_variance_)

This is a mistake in the original answers you linked to. I have edited them accordingly.