scikit-learn: How to calculate root-mean-square er

I have a dataset (found in this link: https://drive.google.com/open?id=0B2Iv8dfU4fTUY2ltNGVkMG05V00) of the following format.

 time     X   Y
0.000543  0  10
0.000575  0  10
0.041324  1  10
0.041331  2  10
0.041336  3  10
0.04134   4  10
  ...
9.987735  55 239
9.987739  56 239
9.987744  57 239
9.987749  58 239
9.987938  59 239

The third column (Y) in my dataset is my true value - that's what I wanted to predict (estimate). I want to do a prediction of Y (i.e. predict the current value of Y according to the previous 100 rolling values of X. For this, I have the following python script work using random forest regression model.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""

@author: deshag
"""

import pandas as pd
import numpy as np
from io import StringIO
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt



df = pd.read_csv('estimated_pred.csv')

for i in range(1,100):
    df['X_t'+str(i)] = df['X'].shift(i)

print(df)

df.dropna(inplace=True)


X=pd.DataFrame({ 'X_%d'%i : df['X'].shift(i) for i in range(100)}).apply(np.nan_to_num, axis=0).values


y = df['Y'].values


reg = RandomForestRegressor(criterion='mse')
reg.fit(X,y)
modelPred = reg.predict(X)
print(modelPred)

print("Number of predictions:",len(modelPred))

meanSquaredError=mean_squared_error(y, modelPred)
print("MSE:", meanSquaredError)
rootMeanSquaredError = sqrt(meanSquaredError)
print("RMSE:", rootMeanSquaredError)

At the end, I measured the root-mean-square error (RMSE) and got an RMSE of 19.57. From what I have read from the documentation, it says that squared errors have the same units as of the response. Is there any way to present the value of an RMSE in percentage? For example, to say this percent of the prediction is correct and this much wrong.

There is a check_array function for calculating mean absolute percentage error (MAPE) in the recent version of sklearn but it doesn't seem to work the same way as the previous version when i try it as in the following.

import numpy as np
from sklearn.utils import check_array

def calculate_mape(y_true, y_pred): 
y_true, y_pred = check_array(y_true, y_pred)

    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

calculate_mape(y, modelPred)

This is returning an error: ValueError: not enough values to unpack (expected 2, got 1). And this seems to be that the check_array function in the recent version returns only a single value, unlike the previous version.

Is there any way to present the RMSE in percentage or calculate MAPE using sklearn for Python?

Your implementation of calculate_mape is not working because you are expecting the check_arrays function, which was removed in sklearn 0.16. check_array is not what you want.

This StackOverflow answer gives a working implementation.