python - Scikit learn + Pandas ValueError : shapes (1, 1) 和 (10,10) 未对齐

Question

我在使用 SciKit Learn 时遇到问题。

我正在做一个非常简单的线性回归问题。根据学习时数和最终成绩的输入值，我希望能够根据学生的学习时长来估计他们的成绩。

In [1]: import pandas as pd
In [2]: path = 'Desktop/hoursgrades.csv'
In [3]: df = pd.read_csv(path)
In [4]: X = df['Hours Studied']
In [5]:  y = df['Grade']
In [6]: training_data_in = list() 
In [7]: training_data_out = list()
In [8]: training_data_in.append(X)
In [9]: training_data_out.append(y)  
In [11]: from sklearn.linear_model import LinearRegression
In [12]: model = LinearRegression(n_jobs =-1) 
In [13]: model.fit(X = training_data_in, y = training_data_out)
Out[13]: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False)

在此示例中，DF 如下所示:

In [16]: df
Out[16]: 
   Hours Studied  Grade
0              1   10.0
1              2   20.0
2              3   30.0
3              4   40.0
4              5   50.0
5              6   60.0
6              7   70.0
7              8   80.0
8              9   90.0
9             10  100.0

X 看起来像这样:

In [17]: X
Out[17]: 
0     1
1     2
2     3
3     4
4     5
5     6
6     7
7     8
8     9
9    10
Name: Hours Studied, dtype: int64

y 看起来像这样:

In [18]: y
Out[18]: 
0     10.0
1     20.0
2     30.0
3     40.0
4     50.0
5     60.0
6     70.0
7     80.0
8     90.0
9    100.0
Name: Grade, dtype: float64

到目前为止一切顺利，它似乎已经接受了我到目前为止所输入的所有内容。所以现在，我想用一些输入数据来测试模型。所以，我想说，这个学生学习的小时数是 5，模型会告诉我预期的成绩。

但是当我将其放入模型中时，出现以下错误。

谁能给点建议吗？

In [14]: studied_hour = [[5]]

In [15]: outcome = model.predict(X = studied_hour)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-15-6fdab4ae2efd> in <module>()
----> 1 outcome = model.predict(X = studied_hour)

~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py in predict(self, X)
    254             Returns predicted values.
    255         """
--> 256         return self._decision_function(X)
    257 
    258     _preprocess_data = staticmethod(_preprocess_data)

~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py in _decision_function(self, X)
    239         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
    240         return safe_sparse_dot(X, self.coef_.T,
--> 241                                dense_output=True) + self.intercept_
    242 
    243     def predict(self, X):

~/anaconda3/lib/python3.7/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)
    138         return ret
    139     else:
--> 140         return np.dot(a, b)
    141 
    142 

ValueError: shapes (1,1) and (10,10) not aligned: 1 (dim 1) != 10 (dim 0)

我应该补充:

In [39]: X.shape
Out[39]: (10,)

In [40]: y.shape
Out[40]: (10,)

Answer 1

X 和 y 的输入形状都不正确，X 必须为 (n_samples, n_features) 和 (n_samples,) 对于 y，按照 docs .

您看到错误是因为模型认为您有十个特征和十个不同的输出(因此是 (10, 10))。

使用以下命令可以获得正确的结果

X = df[['Hours Studied']]  # note the double brackets, shape (10, 1)
y = df['Grade']
model = LinearRegression().fit(X, y)

model.predict([[5]])
array([50.])