View the step-by-step solution to:

# submitted_file_path = &quot;test_knapsack_solution.csv&quot; def Evaluate_Sol(file_path): student_id = file_path.split(&quot;_&quot;)[0] df_solution...

it is a python problem and I am not allowed to import packages .

I am solving a knapsack problem with python,However, my problem is much more complex:

1) 500 knapsack instance, each one has 50 items

2) weight and reward of each item in each knapsack is given

3) the capacity (C)is not a fixed number but a variable follow uniform distribution, U【100,150】

4) only find out C once choose which items to put in the knapsack

5) if we go over C, get nothing for that instance

The screen shot of test cell is in the attachment

submitted_file_path = &quot;test_knapsack_solution.csv&quot; def Evaluate_Sol(file_path):
student_id = file_path.split(&quot;_&quot;)[0]
df_solution.reset_index(inplace = True, drop = True)
current_obj = O for i in range(0,300,3):
sizes = list(df_solution.loc[i, 0:49])
rewards = list(df_solution.loc[i+1, 0:491)
solution = [bool(i) for i in list(df_solution.loc[i+2, 0:49])]
C = knap_sizes[int(i/3)]
#print(sizes)
#print(solution)
total_size = np.dot(sizes,solution)
total_reward = np.dot(rewards,solution)
#print(totai_size)
if total_size &lt;= C: current_obj+=tota1_reward
return {student_id: current_obj} final_resu1ts = Evaluate_Sol(submitted_fi1e_path)
fina1_results

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