from stock_prediction import create_model, load_data, np
from parameters import *
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score

def plot_graph(model, data):
    y_test = data["y_test"]
    X_test = data["X_test"]
    y_pred = model.predict(X_test)
    y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
    y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
    plt.plot(y_test[-200:], c='b')
    plt.plot(y_pred[-200:], c='r')
    plt.xlabel("Days")
    plt.ylabel("Price")
    plt.legend(["Actual Price", "Predicted Price"])
    plt.show()


def get_accuracy(model, data):
    y_test = data["y_test"]
    X_test = data["X_test"]
    y_pred = model.predict(X_test)
    y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
    y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
    y_pred = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_pred[LOOKUP_STEP:]))
    y_test = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_test[LOOKUP_STEP:]))
    return accuracy_score(y_test, y_pred)


def predict(model, data, classification=False):
    # retrieve the last sequence from data
    last_sequence = data["last_sequence"][:N_STEPS]
    # retrieve the column scalers
    column_scaler = data["column_scaler"]
    # reshape the last sequence
    last_sequence = last_sequence.reshape((last_sequence.shape[1], last_sequence.shape[0]))
    # expand dimension
    last_sequence = np.expand_dims(last_sequence, axis=0)
    # get the prediction (scaled from 0 to 1)
    prediction = model.predict(last_sequence)
    # get the price (by inverting the scaling)
    predicted_price = column_scaler["adjclose"].inverse_transform(prediction)[0][0]
    return predicted_price


# load the data
data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE,
                feature_columns=FEATURE_COLUMNS, shuffle=False)

# construct the model
model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,
                    dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)

model_path = os.path.join("results", model_name) + ".h5"
model.load_weights(model_path)

# evaluate the model
mse, mae = model.evaluate(data["X_test"], data["y_test"], verbose=0)
# calculate the mean absolute error (inverse scaling)
mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform(mae.reshape(1, -1))[0][0]
print("Mean Absolute Error:", mean_absolute_error)
# predict the future price
future_price = predict(model, data)
print(f"Future price after {LOOKUP_STEP} days is {future_price:.2f}$")
print("Accuracy Score:", get_accuracy(model, data))
plot_graph(model, data)