Python 尝试在二进制分类上训练SGDClassizer时出现位置参数错误

我正在努力训练一名SGDClassizer

我正在使用MNIST手写数字数据，并通过Anaconda在Jupyter笔记本中运行我的代码。我的anaconda（1.7.0）和sklearn（0.20.dev0）都已更新。我已经粘贴了用于加载数据、选择前60k行、无序排列顺序以及将标签转换为1（True）表示所有5，将标签转换为0（False）表示所有其他数字的代码。X_列和y_列5都是numpy阵列

我已经在下面粘贴了错误消息

数据的维度似乎没有问题，我尝试将X_列转换为稀疏矩阵（SGDClassizer的建议格式）和各种max_iter值，每次都得到相同的错误消息。我错过了什么明显的东西吗？我需要使用不同版本的sklearn吗？我在网上搜索过，但找不到任何描述SGDClassizer类似问题的帖子。我非常感激任何一种指针

代码

from six.moves import urllib
from scipy.io import loadmat
import  numpy as np
from  sklearn.linear_model  import SGDClassifier


# Load MNIST data #

from scipy.io import loadmat
mnist_alternative_url = "https://github.com/amplab/datascience- 
sp14/raw/master/lab7/mldata/mnist-original.mat"
mnist_path = "./mnist-original.mat"
response = urllib.request.urlopen(mnist_alternative_url)
with open(mnist_path, "wb") as f:
    content = response.read()
    f.write(content)
mnist_raw = loadmat(mnist_path)
mnist = {
    "data": mnist_raw["data"].T,
    "target": mnist_raw["label"][0],
    "COL_NAMES": ["label", "data"],
    "DESCR": "mldata.org dataset: mnist-original",
}


# Assign X and y #

X, y = mnist['data'], mnist['target']


# Select first 60000 numbers #

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], 
y[60000:]


# Shuffle order #

shuffle_index  = np.random.permutation(60000)
X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]


# Convert labels to binary (5 or "not 5") #

y_train_5 = (y_train == 5)
y_test_5 = (y_test == 5)

# Train SGDClassifier #

sgd_clf = SGDClassifier(max_iter=5, random_state=42)
sgd_clf.fit(X_train, y_train_5)

---------------------------------------------------------------------------
TypeError
Traceback (most recent call last)
<ipython-input-10-5a25eed28833> in <module>()
     37 # Train SGDClassifier
     38 sgd_clf = SGDClassifier(max_iter=5, random_state=42)
---> 39 sgd_clf.fit(X_train, y_train_5)

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in fit(self, X, y, coef_init, intercept_init, sample_weight)
712                          loss=self.loss, learning_rate=self.learning_rate,
713                          coef_init=coef_init, intercept_init=intercept_init,
--> 714                          sample_weight=sample_weight)
    715 
    716 

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _fit(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)
    570 
    571         self._partial_fit(X, y, alpha, C, loss, learning_rate, self._max_iter,
--> 572                           classes, sample_weight, coef_init, intercept_init)
    573 
    574         if (self._tol is not None and self._tol > -np.inf

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _partial_fit(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init)
    529                              learning_rate=learning_rate,
    530                              sample_weight=sample_weight,
--> 531                              max_iter=max_iter)
    532         else:
    533             raise ValueError(

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _fit_binary(self, X, y, alpha, C, sample_weight, learning_rate, max_iter)
    587                                               self._expanded_class_weight[1],
    588                                               self._expanded_class_weight[0],
--> 589                                               sample_weight)
    590 
    591         self.t_ += n_iter_ * X.shape[0]

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter, pos_weight, neg_weight, sample_weight)
    419                            pos_weight, neg_weight,
    420                            learning_rate_type, est.eta0,
--> 421                            est.power_t, est.t_, intercept_decay)
    422 
    423     else:

~\Anaconda3\lib\site-packages\sklearn\linear_model\sgd_fast.pyx in sklearn.linear_model.sgd_fast.plain_sgd()

TypeError: plain_sgd() takes at most 21 positional arguments (25 given)

错误消息

from six.moves import urllib
from scipy.io import loadmat
import  numpy as np
from  sklearn.linear_model  import SGDClassifier


# Load MNIST data #

from scipy.io import loadmat
mnist_alternative_url = "https://github.com/amplab/datascience- 
sp14/raw/master/lab7/mldata/mnist-original.mat"
mnist_path = "./mnist-original.mat"
response = urllib.request.urlopen(mnist_alternative_url)
with open(mnist_path, "wb") as f:
    content = response.read()
    f.write(content)
mnist_raw = loadmat(mnist_path)
mnist = {
    "data": mnist_raw["data"].T,
    "target": mnist_raw["label"][0],
    "COL_NAMES": ["label", "data"],
    "DESCR": "mldata.org dataset: mnist-original",
}


# Assign X and y #

X, y = mnist['data'], mnist['target']


# Select first 60000 numbers #

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], 
y[60000:]


# Shuffle order #

shuffle_index  = np.random.permutation(60000)
X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]


# Convert labels to binary (5 or "not 5") #

y_train_5 = (y_train == 5)
y_test_5 = (y_test == 5)

# Train SGDClassifier #

sgd_clf = SGDClassifier(max_iter=5, random_state=42)
sgd_clf.fit(X_train, y_train_5)

---------------------------------------------------------------------------
TypeError
Traceback (most recent call last)
<ipython-input-10-5a25eed28833> in <module>()
     37 # Train SGDClassifier
     38 sgd_clf = SGDClassifier(max_iter=5, random_state=42)
---> 39 sgd_clf.fit(X_train, y_train_5)

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in fit(self, X, y, coef_init, intercept_init, sample_weight)
712                          loss=self.loss, learning_rate=self.learning_rate,
713                          coef_init=coef_init, intercept_init=intercept_init,
--> 714                          sample_weight=sample_weight)
    715 
    716 

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _fit(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)
    570 
    571         self._partial_fit(X, y, alpha, C, loss, learning_rate, self._max_iter,
--> 572                           classes, sample_weight, coef_init, intercept_init)
    573 
    574         if (self._tol is not None and self._tol > -np.inf

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _partial_fit(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init)
    529                              learning_rate=learning_rate,
    530                              sample_weight=sample_weight,
--> 531                              max_iter=max_iter)
    532         else:
    533             raise ValueError(

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _fit_binary(self, X, y, alpha, C, sample_weight, learning_rate, max_iter)
    587                                               self._expanded_class_weight[1],
    588                                               self._expanded_class_weight[0],
--> 589                                               sample_weight)
    590 
    591         self.t_ += n_iter_ * X.shape[0]

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter, pos_weight, neg_weight, sample_weight)
    419                            pos_weight, neg_weight,
    420                            learning_rate_type, est.eta0,
--> 421                            est.power_t, est.t_, intercept_decay)
    422 
    423     else:

~\Anaconda3\lib\site-packages\sklearn\linear_model\sgd_fast.pyx in sklearn.linear_model.sgd_fast.plain_sgd()

TypeError: plain_sgd() takes at most 21 positional arguments (25 given)

---------------------------------------------------------------------------
打字错误
回溯（最近一次呼叫最后一次）
在（）
37#列车SGDClassizer
38 sgd\U clf=sgd分类器（最大iter=5，随机状态=42）
--->39 sgd\U clf.fit（X\U系列、y\U系列5）
~\Anaconda3\lib\site packages\sklearn\linear\u model\random\u gradient.py拟合（self，X，y，coef\u init，intercept\u init，sample\u weight）
712损失=自我损失，学习率=自我学习率，
713 coef_init=coef_init，intercept_init=intercept_init，
-->714样品重量=样品重量）
715
716
~\Anaconda3\lib\site packages\sklearn\linear\u model\random\u gradient.py in\u fit（自身、X、y、alpha、C、损耗、学习率、初始系数、初始截距、样本重量）
570
571自我部分拟合（X、y、α、C、损耗、学习率、自我最大值、，
-->572类，样本重量，系数初始值，截距初始值）
573
574如果（self.\u tol不是None和self.\u tol>-np.inf
~\Anaconda3\lib\site packages\sklearn\linear\u model\random\u gradient.py in\u partial\u fit（self，X，y，alpha，C，loss，learning\u rate，max\u iter，classes，sample\u weight，coef\u init，intercept\u init）
529学习率=学习率，
530样品重量=样品重量，
-->531最大热值=最大热值）
532其他：
533上升值错误(
~\Anaconda3\lib\site packages\sklearn\linear\u model\random\u gradient.py in\u fit\u binary（self，X，y，alpha，C，sample\u weight，learning\u rate，max\u iter）
587自扩展类重量[1]，
588自扩展类重量[0]，
-->589样品（单位重量）
590
591 self.t_+=n_iter_*X.shape[0]
~\Anaconda3\lib\site packages\sklearn\linear\u model\randomic\u gradient.py拟合二进制文件（est、i、X、y、alpha、C、学习率、最大值、正权重、负权重、样本权重）
419正重量，负重量，
420学习率类型，est.eta0，
-->421 est功率、est功率、截距衰减）
422
423其他：
~\Anaconda3\lib\site packages\sklearn\linear\u model\sgd\u fast.pyx在sklearn.linear\u model.sgd\u fast.plain\u sgd（）中
TypeError:plain_sgd（）最多接受21个位置参数（给定25个）

您的

scikit learn

版本似乎有点过时。尝试运行：

pip install -U scikit-learn

然后您的代码将运行（进行一些轻微的格式更新）：

---

看起来您在

sklearn

的纯Python（.py）部分和编译后的Cython（.pyx）部分之间存在不匹配。因为从最新版本开始，它需要25个参数。如果运行

pip freeze | grep scikit

，您会得到什么？您可以尝试使用

pip安装-U scikit-learn

更新您的scikit。我就是这么做的，你的代码运行得很好：）谢谢！更新scikit学习包有效。因为我使用的是anaconda，所以我必须使用以下命令（以防其他anaconda用户将来偶然发现此线程）>>conda安装-c anaconda scikit-learn@IsabelHutchison阿门！