我正在学习Aurelien Geron的《Hands-On ML》教材，但在尝试训练SGDClassifier时遇到了困难。
我使用的是MNIST手写数字数据，并通过Anaconda在Jupyter Notebook中运行代码。我的Anaconda版本是1.7.0，sklearn版本是0.20.dev0，都已更新。我已经粘贴了用来加载数据、选择前60,000行、打乱顺序以及将所有数字5的标签转换为1（True），其他数字转换为0（False）的代码。X_train和y_train_5都是numpy数组。
我已经在下面粘贴了错误消息。
数据的维度似乎没有任何问题，我尝试将X_train转换为稀疏矩阵（SGDClassifier建议的格式）和各种max_iter值，但每次都得到相同的错误消息。我是否遗漏了什么明显的东西？我是否需要使用不同的sklearn版本？我在网上搜索过，但没有找到任何描述与SGDClassifier类似问题的帖子。任何指导都会让我非常感激。
代码
from six.moves import urllibfrom scipy.io import loadmatimport numpy as npfrom sklearn.linear_model import SGDClassifier# Load MNIST data #from scipy.io import loadmatmnist_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)错误消息
---------------------------------------------------------------------------TypeErrorTraceback (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)回答：
看起来您的scikit-learn版本有点过时了。尝试运行以下命令：
pip install -U scikit-learn然后您的代码将能够运行（需要进行一些小的格式更新）：
from six.moves import urllibfrom scipy.io import loadmatimport numpy as npfrom sklearn.linear_model import SGDClassifierfrom scipy.io import loadmat# Load MNIST data #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)