Reducing Dimensions using PCA: AttributeError: 'numpy.ndarray' object has no attribute 'items'

Question

I'm trying to implement an example project on DZone (https://dzone.com/articles/cv-r-cvs-retrieval-system-based-on-job-description) and running into a problem. In this case, I've set

dir_pca_we_EWE = 'pickle_model_pca.pkl'

And am executing the following:

def reduce_dimensions_WE(dir_we_EWE, dir_pca_we_EWE):
    m1 = KeyedVectors.load_word2vec_format('./wiki.en/GoogleNews.bin', binary=True)
    model1 = {}
    # normalize vectors
    for string in m1.wv.vocab:
        model1[string] = m1.wv[string] / np.linalg.norm(m1.wv[string])
    # reduce dimensionality
    pca = decomposition.PCA(n_components=200)
    pca.fit(np.array(list(model1.values())))
    model1 = pca.transform(np.array(list(model1.values())))
    i = 0
    for key, value in model1.items():
        model1[key] = model1[i] / np.linalg.norm(model1[i])
        i = i + 1
    with open(dir_pca_we_EWE, 'wb') as handle:
        pickle.dump(model1, handle, protocol=pickle.HIGHEST_PROTOCOL)
return model1

This then produces the following error:

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 12, in reduce_dimensions_WE
AttributeError: 'numpy.ndarray' object has no attribute 'items'

As always, all help is greatly appreciated!

Answer 1

You start by initializing model1 = {} as an empty dict. By using transform in

model1 = pca.transform(np.array(list(model1.values())))

the variable model1 becomes a numpy.ndarray, which is the return type of the transform method of the pca. In the line

for key, value in model1.items():
    ...

you still use model1 as if it is a dict, which it no longer is.