eval.py

import numpy as np
import pandas as pd
import torch
import torch.nn as nn


SCALE = 10000

device = torch.device('mps')
model = nn.Sequential(
    nn.Linear(2, 12),
    nn.ReLU(),
    nn.Linear(12, 12),
    nn.ReLU(),
    nn.Linear(12, 6),
    nn.ReLU(),
    nn.Linear(6, 1)
)
model.load_state_dict(torch.load("orc/A3/weights.pt", map_location=device, weights_only=True))

model.eval()


if __name__ == "__main__":
    df_rand = pd.read_csv('orc/A3/single_pendulum_cost_rand.csv')
    X_rand = df_rand[["q0", "dq0"]].values
    y_rand = df_rand["cost"].values
    # y_rand = y_rand / SCALE

    pred_rand = model(torch.tensor(X_rand, dtype=torch.float32)).detach().numpy() * SCALE
    np.clip(pred_rand, 0.0, None, out=pred_rand)
    # print(pred)


    pred_rand = pred_rand.reshape(-1)
    errors = np.abs((y_rand - pred_rand)) / np.mean(y_rand)
    for i in range(len(y_rand)):
        x0 = X_rand[i]
        real_y = y_rand[i]
        predicted_y = pred_rand[i]
        error = errors[i]
        print(x0, "\t", real_y, "\t", predicted_y, "\t", error)

    print("max error", max(errors))
    errors = np.sort(errors, axis=0)
    print(errors[-10:])