Pandas + sklearn 线性回归失败

Question

我正在尝试在 Python 中实现一些线性回归模型。请参阅下面的代码，我用它来进行线性回归。

import pandas
salesPandas = pandas.DataFrame.from_csv('home_data.csv')

# check the shape of the DataFrame (rows, columns)
salesPandas.shape
(21613, 20)

from sklearn.cross_validation import train_test_split

train_dataPandas, test_dataPandas = train_test_split(salesPandas, train_size=0.8, random_state=1)

from sklearn.linear_model import LinearRegression

reg_model_Pandas = LinearRegression()

print type(train_dataPandas)
print train_dataPandas.shape
<class 'pandas.core.frame.DataFrame'>
(17290, 20)

print type(train_dataPandas['price'])
print train_dataPandas['price'].shape
<class 'pandas.core.series.Series'>
(17290L,)

X = train_dataPandas
y = train_dataPandas['price']
reg_model_Pandas.fit(X, y)

我执行完上面的python代码后，出现如下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-dc363e199032> in <module>()
      3 X = train_dataPandas
      4 y = train_dataPandas['price']
----> 5 reg_model_Pandas.fit(X, y)

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\linear_model\base.py in fit(self, X, y, n_jobs)
    374             n_jobs_ = self.n_jobs
    375         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 376                          y_numeric=True, multi_output=True)
    377 
    378         X, y, X_mean, y_mean, X_std = self._center_data(

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)
    442     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
    443                     ensure_2d, allow_nd, ensure_min_samples,
--> 444                     ensure_min_features)
    445     if multi_output:
    446         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)
    342             else:
    343                 dtype = None
--> 344         array = np.array(array, dtype=dtype, order=order, copy=copy)
    345         # make sure we actually converted to numeric:
    346         if dtype_numeric and array.dtype.kind == "O":

ValueError: invalid literal for float(): 20140610T000000

来自 train_dataPandas.info()

的输出

<class 'pandas.core.frame.DataFrame'>
Int64Index: 17290 entries, 4058200630 to 1762600320
Data columns (total 20 columns):
date             17290 non-null object
price            17290 non-null int64
bedrooms         17290 non-null int64
bathrooms        17290 non-null float64
sqft_living      17290 non-null int64
sqft_lot         17290 non-null int64
floors           17290 non-null float64
waterfront       17290 non-null int64
view             17290 non-null int64
condition        17290 non-null int64
grade            17290 non-null int64
sqft_above       17290 non-null int64
sqft_basement    17290 non-null int64
yr_built         17290 non-null int64
yr_renovated     17290 non-null int64
zipcode          17290 non-null int64
lat              17290 non-null float64
long             17290 non-null float64
sqft_living15    17290 non-null int64
sqft_lot15       17290 non-null int64
dtypes: float64(4), int64(15), object(1)
memory usage: 2.8+ MB

Answer 1

感谢 EdChum，目前的解决方案如下：

首先我上传了数据
salesPandas.info() 向我展示了

Int64Index: 21613 entries, 7129300520 to 1523300157
Data columns (total 20 columns):
date 21613 non-null object

这不好，因为 sklearn 不能将日期用作对象

如果我做 salesPandas.head() 第一个元组的日期是

20141013T000000

你看到T了吗？ ...不好

sklearn.linear_model.LinearRegression().fit() 想要 npy 数组（Pandas 是在 numpy 上构建的，所以 DataFrame 也是一个 numpy 数组）
所以先把对象转成datetime，再转成numeric

salesPandas['date'] = pandas.to_datetime(salesPandas['date'], format='%Y%m%dT%H%M%S')

salesPandas['date'] = pandas.to_numeric(salesPandas['date'])

如果你那么

reg_model_Pandas.fit(X, y)

有效

Answer 2

根据您的数据，另一种可能的解决方案是在从文件中读取日期时指定 parse_dates：

import pandas
salesPandas = pandas.read_csv('home_data.csv', parse_dates=['date'])

之所以有用，是因为当您传递要拟合的数据时，您可以将其分解为月、小时、日。这是假设您的大部分数据都集中在前面提到的那些而不是年份（即您的总独特年份约为 3-4）

从这里您可以使用 Datetimelike Properties 并通过 salesPandas['date'].dt.month 调用月份，然后相应地替换日期和小时。

Pandas + sklearn 线性回归失败

Pandas + sklearn Linear regression fails

python

pandas

scikit-learn