Quantum-Based-Metaheuristics/RBF_Model_Test.py at main · TensorFluids/Quantum-Based-Metaheuristics · GitHub

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import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import RBF
import pandas as pd
from sklearn.metrics import r2_score

# Read and prepare the training data
df = pd.read_csv(r"C:\Users\Vishal\Desktop\Fluid Research\Journal_Paper_Optimization_Codes_And_Data\train3.csv")
obj = df['m']
obj = np.array(obj)
r = df["r"].values
x = df["x"].values
y = df["y"].values

# Normalize the training data
d_normalized = 2 * (r / 909)
x_normalized = x / 909
y_normalized = y / 909

x_normalized = np.column_stack((d_normalized, x_normalized, y_normalized))

# Print the normalized data
print(x_normalized)
print(obj)

# Create and train the RBF model
sm = RBF(d0=19)
sm.set_training_values(x_normalized, obj)
sm.train()

# Read and prepare the test data
df2 = pd.read_csv(r"C:\Users\Vishal\Desktop\Fluid Research\Journal_Paper_Optimization_Codes_And_Data\test3.csv")
X = df2.drop(columns=["m"]).values

# Normalize the test data using the same scaling factors as the training data
d_test_normalized = 2 * (X[:, 0] / 909)
x_test_normalized = X[:, 1] / 909
y_test_normalized = X[:, 2] / 909

X_normalized = np.column_stack((d_test_normalized, x_test_normalized, y_test_normalized))

# Print the normalized test data
print(X_normalized)

num_test_samples = X_normalized.shape[0]

l = []
errors = []

# Make predictions on the normalized test data
for i in range(num_test_samples):
    pred = sm.predict_values(np.array([X_normalized[i]]))
    print(pred)
    l.append(pred[0][0])
    error = (abs((pred - df2['m'][i]) / df2['m'][i]) * 100)
    print("Error = %s" % (error))
    errors.append(error)

for i in range(num_test_samples):
    print(errors[i])

truth = [i for i in df2["m"]]
print("ground truth:" + str(truth))
print("predictions:" + str(l))

r2 = r2_score(l, truth)
r2 = round(r2, 3)
print(r2)

def obj(X):
    pred = sm.predict_values(np.array([X]))
    obj = pred[0][0]
    return obj

def obj_original(X):
    obj = []
    for i in range(25):
        l = []
        l.append(list(X[i]))
        pred = sm.predict_values(np.array(l))
        # print(np.array(l).shape)
        obj.append(pred[0][0])
    obj = np.array(obj)
    return obj

#--------------------------------------------------------------Validation Plot --------------------------------------------------------------------------

plt.figure(figsize=(6.4,4.8),dpi=125)

plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["axes.linewidth"] = 2

a, b = np.polyfit(l, truth, 1)

t = np.linspace(0,1,20)
plt.text(0.11, 0.33, '$R^2$ score = ' + str(r2), fontsize = 17)

plt.plot(t, a*t+b,c = "black",lw = "3" )

plt.scatter(l,truth,c = "red",s=50)
plt.xlim(0.1, 0.35)
plt.ylim(0.1, 0.35)

plt.xlabel("Predicted $C_{p}$ [-]",fontsize=20)
plt.ylabel("Actual $C_{p}$ [-]",fontsize=20)
plt.tick_params(axis='both',size=8,labelsize=16, direction='inout')
plt.show()