python-2.7 - sklearn grid.fit(X,y) - 错误 : “positional indexers are out-of-bounds” for X_train, y_train

Question

这是一个关于 Python 2.7 和 Pandas 0.17.1 中的 scikit learn(版本 0.17.0)的问题。为了使用详细的方法分割原始数据(不丢失条目)here ，我发现如果使用分割数据进行 .fit() 会出现错误。

这里的代码与其他 stackoverflow 问题中的变量重命名基本没有变化。然后，我实例化了一个网格，并尝试拟合分割数据，以确定最佳分类器参数。错误发生在以下代码的最后一行之后:

import pandas as pd
import numpy as np
# UCI's wine dataset
wine = pd.read_csv("https://s3.amazonaws.com/demo-datasets/wine.csv")

# separate target variable from dataset
y = wine['quality']
X = wine.drop(['quality','color'],axis = 1)

# Stratified Split of train and test data
from sklearn.cross_validation import StratifiedShuffleSplit
sss = StratifiedShuffleSplit(y, n_iter=3, test_size=0.2)

# Split dataset to obtain indices for train and test set
for train_index, test_index in sss:
    xtrain, xtest = X.iloc[train_index], X.iloc[test_index]
    ytrain, ytest = y[train_index], y[test_index]

# Pick some classifier here
from sklearn.tree import DecisionTreeClassifier
decision_tree = DecisionTreeClassifier()

from sklearn.grid_search import GridSearchCV
# Instantiate grid
grid = GridSearchCV(decision_tree, param_grid={'max_depth':np.arange(1,3)}, cv=sss, scoring='accuracy')

# this line causes the error message
grid.fit(xtrain,ytrain)

以下是上述代码生成的错误消息:

Traceback (most recent call last):
  File "C:\Python27\test.py", line 23, in <module>
    grid.fit(xtrain,ytrain)
  File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 804, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 553, in _fit
    for parameters in parameter_iterable
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 800, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 658, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 566, in _dispatch
    job = ImmediateComputeBatch(batch)
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 180, in __init__
    self.results = batch()
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", line 1524, in _fit_and_score
    X_train, y_train = _safe_split(estimator, X, y, train)
  File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", line 1591, in _safe_split
    X_subset = safe_indexing(X, indices)
  File "C:\Python27\lib\site-packages\sklearn\utils\__init__.py", line 152, in safe_indexing
    return X.iloc[indices]
  File "C:\Python27\lib\site-packages\pandas\core\indexing.py", line 1227, in __getitem__
    return self._getitem_axis(key, axis=0)
  File "C:\Python27\lib\site-packages\pandas\core\indexing.py", line 1504, in _getitem_axis
    self._is_valid_list_like(key, axis)
  File "C:\Python27\lib\site-packages\pandas\core\indexing.py", line 1443, in _is_valid_list_like
    raise IndexError("positional indexers are out-of-bounds")
IndexError: positional indexers are out-of-bounds

注意: 对我来说，将 X 和 y 保留为 Pandas 数据结构非常重要，类似于上面其他 stackoverflow 问题中提出的第二种方法。即我不想使用 X.values 和 y.values。

问题: 使用原始数据作为 Pandas 数据结构(X 的 DataFrame 和 y 的 Series)，有没有办法运行grid.fit()而不收到此错误消息？

Answer 1

您应该将 X 和 y 直接传递给 fit()，例如

grid.fit(X, y)

和GridSearchCV将负责

xtrain, xtest = X.iloc[train_index], X.iloc[test_index]
ytrain, ytest = y[train_index], y[test_index]

---

StratifiedShuffleSplit 实例在迭代时会生成训练/测试分割对索引:

>>> list(sss)
[(array([2531, 4996, 4998, ..., 3205, 2717, 4983]), array([5942,  893, 1702, ..., 6340, 4806, 2537])),
 (array([1888, 2332, 6276, ..., 1674,  775, 3705]), array([3404, 3304, 4741, ..., 4397, 3646, 1410])),
 (array([1517, 3759, 4402, ..., 5098, 4619, 4521]), array([1110, 4076, 1280, ..., 6384, 1294, 1132]))]

GridSearchCV 将使用这些索引来分割训练样本。您无需手动执行此操作。

发生错误的原因是您将 xtrain 和 ytrain(训练/测试拆分之一)输入到交叉验证器中。交叉验证器尝试访问存在于完整数据集中但不存在于训练/测试拆分中的项目，这会引发 IndexError。