我有一个类似以下的数据集，其中包含大约一千个观测值。我想创建一个在negative和positive类之间分类的SVM超平面图。我使用了基本上从the Scikit-learn website开始采用的后续代码。但是，它给了我这个错误：
ValueError：找到样本数量不一致的输入变量：[2，214]
df_1.head()
text cat lexicon
0 [india, education, commission, report, ma... 1 negative
1 [national, education, policy, 2017, by, min... 2 positive
......
1000 [india, education, commission, report, ma...
我使用以下步骤
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import metrics
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
vectorizer = CountVectorizer(stop_words='english', min_df = 10)
text = df_1.text.tolist()
x = vectorizer.fit_transform(text)
y = df_1.lexicon.tolist()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30,
random_state=0)
fig, ax = plt.subplots()
model = svm.LinearSVC(C=1).fit(x_train, y_train)
# get the separating hyperplane
w = model.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (model.intercept_[0]) / w[1]
# create a mesh to plot in
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
xx2, yy2 = np.meshgrid(np.arange(x_min, x_max, .2),
np.arange(y_min, y_max, .2))
Z = model.predict(np.c_[xx2.ravel(), yy2.ravel()])
Z = Z.reshape(xx2.shape)
ax.contourf(xx2, yy2, Z, cmap=plt.cm.coolwarm, alpha=0.3)
ax.scatter(x[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, s=25)
ax.plot(xx,yy)
ax.axis([x_min, x_max,y_min, y_max])
plt.show()
收到上述错误后，我还尝试使用以下代码转换x和y，但得到类似的错误。有人可以提出解决方案吗？谢谢。
_, y = np.unique(y, return_inverse=True)
x = np.unique(x, return_inverse=True)
详细错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-57-abc7d45a71c2> in <module>()
3 from sklearn import svm
4
----> 5 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=0)
6
7
~\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in train_test_split(*arrays, **options)
2029 test_size = 0.25
2030
-> 2031 arrays = indexable(*arrays)
2032
2033 if shuffle is False:
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables)
227 else:
228 result.append(np.array(X))
--> 229 check_consistent_length(*result)
230 return result
231
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
202 if len(uniques) > 1:
203 raise ValueError("Found input variables with inconsistent numbers of"
--> 204 " samples: %r" % [int(l) for l in lengths])
205
206
ValueError: Found input variables with inconsistent numbers of samples: [2, 214]
但是，如果我删除x_train和y_train，则会出现以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-58-1009080f56c2> in <module>()
22
23 fig, ax = plt.subplots()
---> 24 model = svm.LinearSVC(C=1).fit(x,y)
25
26 # get the separating hyperplane
~\Anaconda3\lib\site-packages\sklearn\svm\classes.py in fit(self, X, y, sample_weight)
225
226 X, y = check_X_y(X, y, accept_sparse='csr',
--> 227 dtype=np.float64, order="C")
228 check_classification_targets(y)
229 self.classes_ = np.unique(y)
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
571 X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
572 ensure_2d, allow_nd, ensure_min_samples,
--> 573 ensure_min_features, warn_on_dtype, estimator)
574 if multi_output:
575 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
431 force_all_finite)
432 else:
--> 433 array = np.array(array, dtype=dtype, order=order, copy=copy)
434
435 if ensure_2d:
ValueError: setting an array element with a sequence.