word2vecを用いて共起ネットワークの作成

Question

word2vecを用いて共起ネットワークの作成を試みています

以下のようなエラーが出てしまいました。

[]
Traceback (most recent call last):
  File "plot_2.py", line 40, in <module>
    pca.fit(df.iloc[:,:-1])
  File "C:\Users\tnaka\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py", line 351, in fit
    self._fit(X)
  File "C:\Users\tnaka\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py", line 397, in _fit
    X = self._validate_data(X, dtype=[np.float64, np.float32],
  File "C:\Users\tnaka\anaconda3\lib\site-packages\sklearn\base.py", line 420, in _validate_data
    X = check_array(X, **check_params)
  File "C:\Users\tnaka\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
    return f(**kwargs)
  File "C:\Users\tnaka\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 533, in check_array
    dtype_orig = np.result_type(*dtypes_orig)
  File "<__array_function__ internals>", line 5, in result_type
ValueError: at least one array or dtype is required

実装しようと思っているコードはこのようなものです。↓

# -*- coding: utf-8 -*-
"""
Created on Thu Jul 30 00:13:40 2020

@author: taiga
"""
# import gensim
# from gensim.models.word2vec import Word2Vec
# model = Word2Vec.load("word2vec.gensim.model")
from gensim.test.utils import common_texts
from gensim.models import Word2Vec

model = Word2Vec(sentences=common_texts, vector_size=100, window=5, min_count=1, workers=4)
model.save("word2vec.model")

model = Word2Vec.load("word2vec.model")
with open("mecab_sanshirou_2.txt",encoding="utf-8_sig") as f:
    words = f.read().splitlines()

re_words = set(words)
re_re=[]
for w in re_words:
    if w in model.wv.key_to_index:
        re_re.append(w)
print(re_re)
wv=[]
for w in re_re:
    wv.append(model[w])
  
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

df = pd.DataFrame(wv)
df["word"] = re_re

#PCAで2次元に圧縮
pca = PCA(n_components=2)
pca.fit(df.iloc[:,:-1])
feature = pca.transform(df.iloc[:,:-1])

#散布図プロット
fp = FontProperties(fname=r"NotoSansCJKjp-Black.otf")
plt.figure(figsize=(10,10))
for x, y, name in zip(feature[:, 0], feature[:, 1], df.iloc[:, -1]):
    plt.text(x, y, name,fontproperties=fp)
plt.scatter(feature[:,0],feature[:,1])
plt.show()

よろしくお願い致します。