问题描述
我想知道使用Scipy适应Pandas DataFrame列的最佳方法.如果我有一个数据表(Pandas DataFrame)的列(A
,B
,C
,D
和Z_real
),其中Z取决于A,B,C和D,我想适合一个预测Z(Z_pred
)的每个DataFrame行(系列)的函数.
I'd like to know the best way to use Scipy to fit Pandas DataFrame columns. If I have a data table (Pandas DataFrame) with columns (A
, B
, C
, D
and Z_real
) where Z depends on A, B, C and D, I want to fit a function of each DataFrame row (Series) which makes a prediction for Z (Z_pred
).
每个要适合的功能的签名是
The signature of each function to fit is
func(series, param_1, param_2...)
其中,系列是与DataFrame的每一行相对应的Pandas系列.我使用Pandas系列,以便不同的功能可以使用不同的列组合.
where series is the Pandas Series corresponding to each row of the DataFrame. I use the Pandas Series so that different functions can use different combinations of columns.
我尝试使用
curve_fit(func, table, table.loc[:, 'Z_real'])
但是由于某种原因,每个func实例都将整个数据表作为其第一个参数传递,而不是将每一行的Series传递给它.我也尝试过将DataFrame转换为Series对象的列表,但是这导致我的函数传递了一个Numpy数组(我想是因为Scipy进行了从Series列表到Numpy数组的转换,这并没有保留熊猫系列对象).
but for some reason each func instance is passed the whole datatable as its first argument rather than the Series for each row. I've also tried converting the DataFrame to a list of Series objects, but this results in my function being passed a Numpy array (I think because Scipy performs a conversion from a list of Series to a Numpy array which doesn't preserve the Pandas Series object).
推荐答案
您对curve_fit
的调用不正确.来自文档:
Your call to curve_fit
is incorrect. From the documentation:
测量数据的自变量.
ydata : M长度序列
从属数据-名义上为f(xdata,...)
The dependent data — nominally f(xdata, ...)
在这种情况下,您的因变量 xdata
是列A到D,即table[['A', 'B', 'C', 'D']]
,而您的因变量 ydata
是table['Z_real']
.
In this case your independent variables xdata
are the columns A to D, i.e. table[['A', 'B', 'C', 'D']]
, and your dependent variable ydata
is table['Z_real']
.
还要注意,xdata
应该是一个(k,M)数组,其中 k 是预测变量(即列)的数量,而 M 是观测值(即行)的数量.因此,您应该对输入数据帧进行转置,以使其为(4,M),而不是(M,4),即table[['A', 'B', 'C', 'D']].T
.
Also note that xdata
should be a (k, M) array, where k is the number of predictor variables (i.e. columns) and M is the number of observations (i.e. rows). You should therefore transpose your input dataframe so that it is (4, M) rather than (M, 4), i.e. table[['A', 'B', 'C', 'D']].T
.
对curve_fit
的整个调用可能看起来像这样:
The whole call to curve_fit
might look something like this:
curve_fit(func, table[['A', 'B', 'C', 'D']].T, table['Z_real'])
这是显示多元线性回归的完整示例:
Here's a complete example showing multiple linear regression:
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
X = np.random.randn(100, 4) # independent variables
m = np.random.randn(4) # known coefficients
y = X.dot(m) # dependent variable
df = pd.DataFrame(np.hstack((X, y[:, None])),
columns=['A', 'B', 'C', 'D', 'Z_real'])
def func(X, *params):
return np.hstack(params).dot(X)
popt, pcov = curve_fit(func, df[['A', 'B', 'C', 'D']].T, df['Z_real'],
p0=np.random.randn(4))
print(np.allclose(popt, m))
# True
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