我在泰坦尼克号模型上使用逻辑回归,PyCharm 要求我仅传递带有 bool 值的 DataFrame:
Traceback (most recent call last):
File "C:/Users/security/Downloads/AP/Titanic-Kaggle/TItanic-Kaggle.py", line 29, in <module>
predictions = logReg.predict(test[test_data])
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\pandas\core\frame.py", line 2914, in __getitem__
return self._getitem_frame(key)
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\pandas\core\frame.py", line 3009, in _getitem_frame
raise ValueError('Must pass DataFrame with boolean values only')
ValueError: Must pass DataFrame with boolean values only
我不明白为什么,因为在训练模型时,逻辑回归使用了完全相同的特征,并且当时很受欢迎。这是我的代码(忽略代码重复。这是我稍后要解决的问题):
import pandas as pd
import warnings
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
warnings.filterwarnings("ignore", category=FutureWarning)
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")
train['Sex'] = train['Sex'].replace(['female', 'male'], [0, 1])
train['Embarked'] = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
train['Age'].fillna(train.groupby('Sex')['Age'].transform("median"), inplace=True)
train['HasCabin'] = train['Cabin'].notnull().astype(int)
train['Relatives'] = train['SibSp'] + train['Parch']
train_data = train[['Pclass', 'Sex', 'Relatives', 'Fare', 'Age', 'Embarked', 'HasCabin']]
x_train, x_validate, y_train, y_validate = train_test_split(train_data, train['Survived'], test_size=0.22, random_state=0)
test['Sex'] = test['Sex'].replace(['female', 'male'], [0, 1])
test['Embarked'] = test['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
test['Age'].fillna(test.groupby('Sex')['Age'].transform("median"), inplace=True)
test['HasCabin'] = test['Cabin'].notnull().astype(int)
test['Relatives'] = test['SibSp'] + test['Parch']
test_data = test[['Pclass', 'Sex', 'Relatives', 'Fare', 'Age', 'Embarked', 'HasCabin']]
logReg = LogisticRegression()
logReg.fit(x_train, y_train)
predictions = logReg.predict(test[test_data])
submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': predictions})
filename = 'Titanic-Submission.csv'
submission.to_csv(filename, index=False)
具体来说,Python 对此片段有疑问:
test_data = test[['Pclass', 'Sex', 'Relatives', 'Fare', 'Age', 'Embarked', 'HasCabin']]
...
predictions = logReg.predict(test[test_data])
更新
我已将我的预测
变量更改为:
预测 = logReg.predict(test_data)
现在这是我的堆栈跟踪:
Traceback (most recent call last):
File "C:/Users/security/Downloads/AP/Titanic-Kaggle/TItanic-Kaggle.py", line 29, in <module>
predictions = logReg.predict(test_data)
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\sklearn\linear_model\base.py", line 281, in predict
scores = self.decision_function(X)
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\sklearn\linear_model\base.py", line 257, in decision_function
X = check_array(X, accept_sparse='csr')
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\sklearn\utils\validation.py", line 573, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "C:\Users\security\Anaconda3\envs\TItanic-Kaggle.py\lib\site-packages\sklearn\utils\validation.py", line 56, in _assert_all_finite
raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
这意味着我对测试数据的特征选择/工程没有通过
最佳答案
使用x_validate
进行预测没有问题。尝试:
>>> predictions = logReg.predict(x_validate)
所以 test_data
肯定有问题。获取有关数据帧的一些信息并进行比较:
>>> x_validate.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 197 entries, 495 to 45
Data columns (total 7 columns):
Pclass 197 non-null int64
Sex 197 non-null int64
Relatives 197 non-null int64
Fare 197 non-null float64
Age 197 non-null float64
Embarked 197 non-null int64
HasCabin 197 non-null int64
dtypes: float64(2), int64(5)
memory usage: 12.3 KB
>>> test_data.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 7 columns):
Pclass 418 non-null int64
Sex 418 non-null int64
Relatives 418 non-null int64
Fare 417 non-null float64
Age 418 non-null float64
Embarked 418 non-null int64
HasCabin 418 non-null int64
dtypes: float64(2), int64(5)
memory usage: 22.9 KB
看起来这里有一个 NaN:
Fare 417 non-null float64
关于python - 如何对测试数据使用逻辑回归,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56489120/