model_eval.py

# here we re-initialize dataloader so the data doesn't shuffled, so we can plot the values by date

train_dataloader = DataLoader(dataset_train, batch_size=config["training"]["batch_size"], shuffle=False)
val_dataloader = DataLoader(dataset_val, batch_size=config["training"]["batch_size"], shuffle=False)

model.eval()

# predict on the training data, to see how well the model managed to learn and memorize

predicted_train = np.array([])

for idx, (x, y) in enumerate(train_dataloader):
    x = x.to(config["training"]["device"])
    out = model(x)
    out = out.cpu().detach().numpy()
    predicted_train = np.concatenate((predicted_train, out))

# predict on the validation data, to see how the model does

predicted_val = np.array([])

for idx, (x, y) in enumerate(val_dataloader):
    x = x.to(config["training"]["device"])
    out = model(x)
    out = out.cpu().detach().numpy()
    predicted_val = np.concatenate((predicted_val, out))

# prepare data for plotting

to_plot_data_y_train_pred = np.zeros(num_data_points)
to_plot_data_y_val_pred = np.zeros(num_data_points)

to_plot_data_y_train_pred[config["data"]["window_size"]:split_index+config["data"]["window_size"]] = scaler.inverse_transform(predicted_train)
to_plot_data_y_val_pred[split_index+config["data"]["window_size"]:] = scaler.inverse_transform(predicted_val)

to_plot_data_y_train_pred = np.where(to_plot_data_y_train_pred == 0, None, to_plot_data_y_train_pred)
to_plot_data_y_val_pred = np.where(to_plot_data_y_val_pred == 0, None, to_plot_data_y_val_pred)

# plots

fig = figure(figsize=(25, 5), dpi=80)
fig.patch.set_facecolor((1.0, 1.0, 1.0))
plt.plot(data_date, data_close_price, label="Actual prices", color=config["plots"]["color_actual"])
plt.plot(data_date, to_plot_data_y_train_pred, label="Predicted prices (train)", color=config["plots"]["color_pred_train"])
plt.plot(data_date, to_plot_data_y_val_pred, label="Predicted prices (validation)", color=config["plots"]["color_pred_val"])
plt.title("Compare predicted prices to actual prices")
xticks = [data_date[i] if ((i%config["plots"]["xticks_interval"]==0 and (num_data_points-i) > config["plots"]["xticks_interval"]) or i==num_data_points-1) else None for i in range(num_data_points)] # make x ticks nice
x = np.arange(0,len(xticks))
plt.xticks(x, xticks, rotation='vertical')
plt.grid(b=None, which='major', axis='y', linestyle='--')
plt.legend()
plt.show()