adjust_predictions4nighbourhood.py

def adjust_predictions4nighbourhood(y_test, predict_test):
    slack = 5
    y_test = y_test.values
    length = len(y_test)
    adjusted_forecasts = np.copy(predict_test)
    for i in range(length):
        if y_test[i] == predict_test[i]:
            adjusted_forecasts[i] = predict_test[i]
        elif predict_test[i] == 1: #FP
            if np.sum(y_test[i-slack:i+slack]) > 0:
                #print(y_test[i - slack:i + slack], "=", np.sum(y_test[i - slack:i + slack]))
                adjusted_forecasts[i] = 0 #there is anomaly within 20 in actual, so 1 OK
        elif predict_test[i] == 0:  # FN
            if np.sum(predict_test[i-slack:i+slack]) > 0:
                #print(predict_test[i - slack:i + slack], "=", np.sum(predict_test[i - slack:i + slack]))
                adjusted_forecasts[i] = 1 #there is anomaly within 20 in predicted, so OK

    return adjusted_forecasts