Multi-input single output regression using Scikit neural networks MLPRegressor

Question

I have a 2D-array data as follows (with M & C as independent variables):

Data

I'm trying to model this regression (f(M,C) = y) using the Scikit MLPRegressor. Not knowing how to go about modeling multivariable input, I tried modeling it as two independent single-input problems. How do I use this output to predict the curve for C=2.3, for example? If anyone can suggest a more elegant way to program this problem, I'd be thankful.

Code

# importing module
import numpy as np
import pandas as pd
from pandas import *
from matplotlib import pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor

# reading CSV file
#data = read_csv("data\Ex2.csv")
data = pd.DataFrame([[1,1,1],[2,4.28,5.65],[3,10.04,15.59],[4,18.38,32],[5,29.36,55.90],[6,43.06,88.18],[7,59.52,129.64],[8,78.79,181.02],[9,100.90,243],[10,125.89,316.22]], columns=['M','C=2.1','C=2.5'])

# converting column data to list
M = data['M'].values
C1 = data['C=2.1'].values
C2 = data['C=2.5'].values
C = np.column_stack((C1,C2))

M = M.reshape(M.shape[0],-1)

for i in range(0, 2):

    Xtrain, Xtest, Ytrain, Ytest = train_test_split(M, C[:,i], test_size=0.2, random_state=42)            
    mlp = MLPRegressor(random_state=42, activation='relu', hidden_layer_sizes=(100,100,100,100,100,100,100,100,8))    
    
    mlp.fit(Xtrain, Ytrain.ravel())    
    Yguess = mlp.predict(Xtest)
    Y = mlp.predict(M)
    
    #plt.plot(Xtest[:,0], Ytest, '.')
    #plt.plot(Xtest[:,0], Yguess, 'r.')
    plt.plot(M, C[:,i], 'o')
    plt.plot(M, Y, 'r')
 

Result

Answer 1

I was able to solve the problem as below. Thank you @l'mahdi for your help and to @D.L for your comments

Code solution

# importing module
import numpy as np
import pandas as pd
from pandas import *
from matplotlib import pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor

# reading CSV file
data = pd.DataFrame([[1,1,1],[2,4.28,5.65],[3,10.04,15.59],[4,18.38,32],[5,29.36,55.90],[6,43.06,88.18],[7,59.52,129.64],[8,78.79,181.02],[9,100.90,243],[10,125.89,316.22]], columns=['M','C=2.1','C=2.5'])

# converting column data to array
M = data['M'].values
C1 = data['C=2.1'].values
C2 = data['C=2.5'].values

C = np.concatenate((C1,C2))
I = np.full_like(M,1)
MC1 = np.column_stack((M,I*2.1))
MC2 = np.column_stack((M,I*2.5))
MC3 = np.column_stack((M,I*2.4))
MC = np.concatenate((MC1,MC2))

MC = MC.reshape(MC.shape[0],-1)

mlp = MLPRegressor(random_state=0, activation='relu', hidden_layer_sizes=(100,100,100,100,100,100,100,100,100,100,100,100,8))    

mlp.fit(MC,C)  
Y = mlp.predict(MC3)
    
plt.plot(M, C1)
plt.plot(M, C2)
plt.plot(M, Y, 'r')

Result