使用 pandas 计算数据帧时出现罕见错误

Question

我在使用 Pandas 加载的数据集进行机器学习时遇到了一个罕见的错误。 这是我得到的错误：

我一直在阅读与它相关的内容，这似乎是由于这些列以及熊猫如何解释它们，但我不知道可能出了什么问题。 这是我为此使用的代码：

import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-
indians-diabetes/pima-indians-diabetes.data'
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 
'pedigree', 'age', 'label']
pima = pd.read_csv(url, header=None, names=col_names)
# define X and y
feature_cols = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 
'pedigree', 'age']
X = pima[feature_cols]
y = pima.label
#k fold cv
from sklearn.model_selection import KFold, cross_val_score
kf = KFold(n_splits=10) #define number of splits
kf.get_n_splits(X) #to check how many splits will be done.
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
clf = LinearDiscriminantAnalysis() #select the model for train, test in kf.split(X, y):
for train, test in kf.split(X, y):
    y_pred_prob = clf.fit(X[train], y[train]).predict_proba(X[test])
    y_pred_class = clf.predict(X[test])

提前致谢

Answer 1

在clf.fit方法中，根据文档参数预期为array：

Parameters
----------
X : array-like, shape (n_samples, n_features)
    Training data.

y : array, shape (n_samples,)

如果您查看link 中的示例，X 和y 是numpy array： 尝试将as_matrix() 用于X 和y 而不是仅X = pima[feature_cols] 和y = pima.label

X = pima[feature_cols].as_matrix()
y = pima.label.as_matrix()

print 测试：

import pandas as pd

url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data'
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv(url, header=None, names=col_names)

# define X and y
feature_cols = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age']
X = pima[feature_cols].as_matrix()
y = pima.label.as_matrix()

#k fold cv
from sklearn.model_selection import KFold, cross_val_score
kf = KFold(n_splits=10) #define number of splits
kf.get_n_splits(X) #to check how many splits will be done.
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
clf = LinearDiscriminantAnalysis() #select the model for train, test in kf.split(X, y):

for train, test in kf.split(X, y):
    y_pred_prob = clf.fit(X[train], y[train]).predict_proba(X[test])
    y_pred_class = clf.predict(X[test])
    print(y_pred_class)

结果：

[1 0 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 0 0 1
 0 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
 0 0 0]
[0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0
 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
 0 1 1]
[1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0
 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1
 1 0 1]
[1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 1 1
 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0
 0 1 0]
[0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0
 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0
 0 0 0]
[0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0
 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1
 1 0 0]
[0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1
 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
 0 0 0]
[0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1
 0 1 0]
[0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0
 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1
 0 1]
[0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0
 0 0]