在不使用额外的库或方法的情况下将 pandas 个数据框拟合到 Scikit-Learn 的模型

Question

一方面，people say pandas goes along great with scikit-learn. For example, pandas series objects fit well with sklearn models in this video。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架起了一座桥梁，这意味着需要对于这样的图书馆。此外，例如，有些人将 pandas 数据帧转换为 numpy 数组以拟合模型。

我想知道是否可以将 pandas 和 scikit-learn 结合起来而不需要任何额外的方法和库。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。但是网上很少有使用类似代码没有任何问题的例子。

Answer 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 binary 输出或 multiclass one。在您的情况下，您将 continuous 数据提供给 classifier.

如果您追溯您的错误并深入研究 sklearn 方法 .fit() 的实现，您会发现以下函数：

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)

函数 type_of_target 的文档字符串是：

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 aValueErrorin the functioncheck_classification_targets()`.

结论：

如果您想执行分类，请更改您的目标y。（例如，使用二元向量）
如果您想保持连续数据，请执行回归。使用 svm.SVR.

在不使用额外的库或方法的情况下将 pandas 个数据框拟合到 Scikit-Learn 的模型

Fitting pandas Data Frames to Scikit-Learn’s model without using additional libraries or methods

python

python-3.x

pandas

scikit-learn

sklearn-pandas