我目前正在观看一个关于对KDD 99杯数据集应用机器学习算法的视频。当我运行下面的代码时，出现了“无法将字符串转换为浮点数 ‘normal’”的错误。’normal’ 是下面显示的Y特征集中找到的标签之一。Y特征集有23个标签，当我测试算法仅预测3个Y特征（normal, smurf和neptune）时，它工作得非常好，但是一旦我尝试让它预测所有标签，我就会得到这个错误。任何指导都将不胜感激，因为我已经为此工作了两天了。
feature_cols =['duration','src_bytes','dst_bytes','land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'label', 'proto__icmp', 'proto__tcp', 'proto__udp']x = dataset[feature_cols]y = dataset.labely.value_counts(normalize=True)Y特征标签
smurf. neptune. normal. back. satan. ipsweep. portsweep. warezclient. teardrop. pod. nmap. guess_passwd.buffer_overflow.land.warezmaster.imap.rootkit.loadmodule.ftp_write.multihop.phf.perl.spy.Name: label, dtype: float64代码和错误
from sklearn.tree import DecisionTreeClassifierdt = DecisionTreeClassifier()scores = cross_val_score(dt, x, y, scoring='accuracy', cv=10)print (scores)print ("Accuracy: %2.10f" % np.mean(scores))ValueError Traceback (most recent call last)<ipython-input-70-722f95b657f5> in <module>() 1 from sklearn.tree import DecisionTreeClassifier 2 dt = DecisionTreeClassifier()----> 3 scores = cross_val_score(dt, x, y, scoring='accuracy', cv=10) 4 print (scores) 5 print ("Accuracy: %2.10f" % np.mean(scores))~\Anaconda3\lib\site-packages\sklearn\cross_validation.py in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch) 1579 train, test, verbose, None, 1580 fit_params)-> 1581 for train, test in cv) 1582 return np.array(scores)[:, 0] 1583 ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable) 777 # was dispatched. In particular this covers the edge 778 # case of Parallel used with an exhausted iterator.--> 779 while self.dispatch_one_batch(iterator): 780 self._iterating = True 781 else:~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator) 623 return False 624 else:--> 625 self._dispatch(tasks) 626 return True 627 ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch) 586 dispatch_timestamp = time.time() 587 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)--> 588 job = self._backend.apply_async(batch, callback=cb) 589 self._jobs.append(job) 590 ~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback) 109 def apply_async(self, func, callback=None): 110 """Schedule a func to be run"""--> 111 result = ImmediateResult(func) 112 if callback: 113 callback(result)~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch) 330 # Don't delay the application, to avoid keeping the input 331 # arguments in memory--> 332 self.results = batch() 333 334 def get(self):~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self) 129 130 def __call__(self):--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] 132 133 def __len__(self):~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0) 129 130 def __call__(self):--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] 132 133 def __len__(self):~\Anaconda3\lib\site-packages\sklearn\cross_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score) 1673 estimator.fit(X_train, **fit_params) 1674 else:-> 1675 estimator.fit(X_train, y_train, **fit_params) 1676 1677 except Exception as e:~\Anaconda3\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted) 788 sample_weight=sample_weight, 789 check_input=check_input,--> 790 X_idx_sorted=X_idx_sorted) 791 return self 792 ~\Anaconda3\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted) 114 random_state = check_random_state(self.random_state) 115 if check_input:--> 116 X = check_array(X, dtype=DTYPE, accept_sparse="csc") 117 y = check_array(y, ensure_2d=False, dtype=None) 118 if issparse(X):~\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: could not convert string to float: 'normal.'完整代码如要求
import pandas as pdimport warningswarnings.filterwarnings('ignore')col_names = ["duration","protocol_type","service","flag","src_bytes", "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins", "logged_in","num_compromised","root_shell","su_attempted","num_root", "num_file_creations","num_shells","num_access_files","num_outbound_cmds", "is_host_login","is_guest_login","count","srv_count","serror_rate", "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate", "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count", "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate", "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate", "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]dataset = pd.read_csv('../data/kddcup.data', header=None, names=col_names)# 警告，加载需要一段时间# 为协议类型创建虚拟变量protocol_dummies = pd.get_dummies(dataset['protocol_type'], prefix='proto_')# 将虚拟变量列连接到原始DataFrame上（axis=0表示行，axis=1表示列）dataset = pd.concat([dataset, protocol_dummies], axis=1)del dataset['protocol_type']x = dataset.drop(['label'], axis=1)y = dataset.labelfrom sklearn.cross_validation import cross_val_scorefrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.linear_model import LogisticRegressionimport numpy as npfrom sklearn.cross_validation import train_test_splitfrom datetime import datetimefeature_cols =['duration','src_bytes','dst_bytes','land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'label', 'proto__icmp', 'proto__tcp', 'proto__udp']x = dataset[feature_cols]y = dataset.labelfrom sklearn.tree import DecisionTreeClassifierdt = DecisionTreeClassifier()scores = cross_val_score(dt, x, y, scoring='accuracy', cv=10)print (scores)print ("Accuracy: %2.10f" % np.mean(scores))KDD数据集中的一行
0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal.
回答：
我刚刚意识到我在x特征中保留了标签列。我已经将其移除，现在它可以工作了。
