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一方面，人们说 pandas与scikit-learn配合得很好。例如，pandas 系列对象非常适合本视频中的 sklearn 模型。另一方面，sklearn-pandas在 Scikit-Learn 的机器学习方法和 pandas 风格的数据框架之间提供了一座桥梁，这意味着需要这样的库。此外，例如，有些人将 pandas 数据帧转换为 numpy 数组以拟合模型。

我想知道是否可以在没有任何其他方法和库的情况下将pandas和scikit-learn结合起来。我的问题是，每当我通过以下方式将我的数据集拟合到 sklearn 模型时：

import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC

d = {'x': np.linspace(1., 100., 20), 'y': np.linspace(1., 10., 20)}
df = pd.DataFrame(d)

train, test = train_test_split(df, test_size = 0.2)

trainX = train['x']
trainY = train['y']

lin_svm = SVC(kernel='linear').fit(trainX, trainY)

我收到一个错误：

ValueError: Unknown label type: 19    10.000000
0      1.000000
17     9.052632
18     9.526316
12     6.684211
11     6.210526
16     8.578947
14     7.631579
10     5.736842
7      4.315789
8      4.789474
2      1.947368
13     7.157895
1      1.473684
6      3.842105
3      2.421053
Name: y, dtype: float64

据我了解，这是因为数据结构。但是，互联网上使用类似代码没有任何问题的示例很少。

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1 回答 1

1

您可能想要做的是回归而不是分类。

想一想，要进行分类，您需要二进制输出或多类输出。在您的情况下，您将连续数据提供给您的分类器。

如果你追溯你的错误并深入挖掘sklearn's 方法的实现，.fit()你会发现以下函数：

def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.

Only the following target types (as defined in type_of_target) are allowed:
    'binary', 'multiclass', 'multiclass-multioutput', 
    'multilabel-indicator', 'multilabel-sequences'

Parameters
----------
y : array-like
"""
y_type = type_of_target(y)
if y_type not in ['binary', 'multiclass', 'multiclass-multioutput', 
        'multilabel-indicator', 'multilabel-sequences']:
    raise ValueError("Unknown label type: %r" % y)

该函数的文档字符串type_of_target为：

def type_of_target(y):
"""Determine the type of data indicated by target `y`

Parameters
----------
y : array-like

Returns
-------
target_type : string
    One of:
    * 'continuous': `y` is an array-like of floats that are not all
      integers, and is 1d or a column vector.
    * 'continuous-multioutput': `y` is a 2d array of floats that are
      not all integers, and both dimensions are of size > 1.
    * 'binary': `y` contains <= 2 discrete values and is 1d or a column
      vector.
    * 'multiclass': `y` contains more than two discrete values, is not a
      sequence of sequences, and is 1d or a column vector.
    * 'multiclass-multioutput': `y` is a 2d array that contains more
      than two discrete values, is not a sequence of sequences, and both
      dimensions are of size > 1.
    * 'multilabel-indicator': `y` is a label indicator matrix, an array
      of two dimensions with at least two columns, and at most 2 unique
      values.
    * 'unknown': `y` is array-like but none of the above, such as a 3d
      array, sequence of sequences, or an array of non-sequence objects.

在你的情况下type_of_target(trainY)=='continuous' and then it raises aValueError in the functioncheck_classification_targets()`。

结论：

  • 如果要执行分类，请更改目标y。（例如，使用二进制向量）
  • 如果你想保持你的连续数据执行回归。使用svm.SVR.
于 2016-10-07T12:51:16.750 回答