scikit-learn fit() 在规范化数据后导致错误

Question

我一直在尝试这个：

根据数据集创建 X 特征和 y 特征
拆分数据集
标准化数据
使用 Scikit-learn 中的 SVR 进行训练

下面是使用 pandas 数据框填充随机值的代码

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(20,5), columns=["A","B","C","D", "E"])
a = list(df.columns.values)
a.remove("A")

X = df[a]
y = df["A"]

X_train = X.iloc[0: floor(2 * len(X) /3)]
X_test = X.iloc[floor(2 * len(X) /3):]
y_train = y.iloc[0: floor(2 * len(y) /3)]
y_test = y.iloc[floor(2 * len(y) /3):]

# normalise

from sklearn import preprocessing

X_trainS = preprocessing.scale(X_train)
X_trainN = pd.DataFrame(X_trainS, columns=a)

X_testS = preprocessing.scale(X_test)
X_testN = pd.DataFrame(X_testS, columns=a)

y_trainS = preprocessing.scale(y_train)
y_trainN = pd.DataFrame(y_trainS)

y_testS = preprocessing.scale(y_test)
y_testN = pd.DataFrame(y_testS)

import sklearn
from sklearn.svm import SVR

clf = SVR(kernel='rbf', C=1e3, gamma=0.1)

pred = clf.fit(X_trainN,y_trainN).predict(X_testN)

出现此错误：

C:\Anaconda3\lib\site-packages\pandas\core\index.py:542: FutureWarning: slice indexers when using iloc should be integers and not floating point "and not floating point",FutureWarning) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 34 clf = SVR(kernel='rbf', C=1e3, gamma=0.1) 35 ---> 36 pred = clf.fit(X_trainN,y_trainN).predict(X_testN) 37

C:\Anaconda3\lib\site-packages\sklearn\svm\base.py in fit(self, X, y, sample_weight) 174 175 seed = rnd.randint(np.iinfo('i').max) --> 176 fit(X, y, sample_weight, solver_type, kernel, random_seed=seed) 177 # see comment on the other call to np.iinfo in this file 178

C:\Anaconda3\lib\site-packages\sklearn\svm\base.py in _dense_fit(self, X, y, sample_weight, solver_type, kernel, random_seed) 229 cache_size=self.cache_size, coef0=self.coef0, 230 gamma=self._gamma, epsilon=self.epsilon, --> 231 max_iter=self.max_iter, random_seed=random_seed) 232 233 self._warn_from_fit_status()

C:\Anaconda3\lib\site-packages\sklearn\svm\libsvm.pyd in sklearn.svm.libsvm.fit (sklearn\svm\libsvm.c:1864)()

ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

我不知道为什么。谁能解释一下？我认为这与预处理后转换回数据帧有关。

Answer 1

此处的错误在您作为标签传递的 df 中：y_trainN

如果您与 sample docs 版本和您的代码进行比较：

In [40]:

n_samples, n_features = 10, 5
np.random.seed(0)
y = np.random.randn(n_samples)
print(y)
y_trainN.values
[ 1.76405235  0.40015721  0.97873798  2.2408932   1.86755799 -0.97727788
  0.95008842 -0.15135721 -0.10321885  0.4105985 ]
Out[40]:
array([[-0.06680594],
       [ 0.23535043],
       [-1.49265082],
       [ 1.22537862],
       [-0.46499134],
       [-0.23744759],
       [ 1.40520679],
       [ 0.95882677],
       [ 1.66996413],
       [-0.37515955],
       [-0.75826444],
       [-1.45945337],
       [-0.63995369]])

因此您可以调用 squeeze 来生成一个系列，或者 select df 中的唯一列以便没有错误：

pred = clf.fit(X_trainN,y_trainN[0]).predict(X_testN)

或

pred = clf.fit(X_trainN,y_trainN.squeeze()).predict(X_testN)

所以我们可以争辩说，对于只有一个列的 df，它应该 return 然后可以强制转换为 numpy 数组的东西，或者 numpy 没有正确调用数组属性，但实际上你应该通过一个系列或 select 来自 df 的列作为参数

scikit-learn fit() 在规范化数据后导致错误

sckit-learn fit() leads to error after normalising the data

python

numpy

svm

pandas

scikit-learn