Python - Find coefficients minimizing error in csv data

I've recently run into a problem. I have data looking like this :

Value 1	Value 2	Target
1345	4590	2.45
1278	3567	2.48
1378	4890	2.46
1589	4987	2.50
...	...	...

The data goes on for a few thousand lines.

I need to find two values (A & B), that minimize the error when the data is inputted like so :

Value 1 * A + Value 2 * B = Target

I've looked into scipy.optimize.curve_fit, but I can't seem to understand how it would work, because the function changes at every iteration of the data (since Value 1 and Value 2 are not the same over every row).

Any help is greatly appreciated, thanks in advance !

Solution

Unfortunataly you have not provided any test data so I have come up with my own:

import pandas as pd
import numpy as np
from scipy.optimize import minimize
import matplotlib.pyplot as plt

def f(V1,V2,A,B): #Target function
    return V1*A+V2*B

# Generate Test-Data
def generateData(A,B): 
    np.random.seed(0)
    V1=np.random.uniform(low=1000, high=1500, size=(100,))
    V2=np.random.uniform(low=3500, high=5000, size=(100,))
    Target=f(V1,V2,A,B) +np.random.normal(0,1,100)
    return V1,V2,Target
data=generateData(2,3) #Important: 
data={"Value 1":data[0], "Value 2":data[1], "Target":data[2]}
df=pd.DataFrame(data) #Similar structure as given in Table

df.head() looks like this:

    Value 1 Value 2 Target
0   1292.0525763109854  3662.162080896163   13570.276523473405
1   1155.0421489258965  4907.133274663096   17033.392287295104
2   1430.7172112685223  4844.422515098364   17395.412651006143
3   1396.0480757043242  4076.5845114488666  15022.720636830541
4   1346.2120476329646  3570.9567326419674  13406.565815022896

Your question is answered in the following:

## Plot Data to check whether linear function is useful 

df.head()
fig=plt.figure()
ax1=fig.add_subplot(211)
ax2=fig.add_subplot(212)
ax1.scatter(df["Value 1"], df["Target"])
ax2.scatter(df["Value 2"], df["Target"])



def fmin(x, df): #Returns Error at given parameters
    def RMSE(y,y_target): #Definition for error term 
        return np.sqrt(np.mean((y-y_target)**2))
    A,B=x
    V1,V2,y_target=df["Value 1"], df["Value 2"], df["Target"]
    y=f(V1,V2,A,B) #Calculate target value with given parameter set
    return RMSE(y,y_target)

res=minimize(fmin,x0=[1,1],args=df, options={"disp":True})
print(res.x)

I prefere scipy.optimize.minimize() over curve_fit since you can define the error function yourself. The documentation can be found here. You need:

a function fun that returns the error for a given set of parameter x (here fmin with RMSE)
an initial guess x0 (here [1,1]), if your guess is totally off you will probably do not find a solution or (with more complex problems) just a local one
additional arguments args provided to the fun here the data df but also helpful for fixed parameters
options={"disp":True} is for printing additional information
your parameters can be found besides further information in the returned variable res

For this case the result is:

[1.9987209 3.0004212]

Similar to the given parameters when generating the data.