[Python] Prediction on new data after pickling on regression model

I have created ridge regression model to predict sales of an item say X. My final model contains around 180 features. I have pickled this model and now I want to see how model I created is performing on new data set containing same features as present in different model but on different timeframe. I have to pass entire new dataset(dataframe) into existing model and check relevant score say r-square or any other score relevant to regression model.

Below is the code I'm using:

# loading library
import pickle

with open('ridge_model', 'wb') as p:
pickle.dump(ridge, p)

y_test = df.pop('Target')
x_test = df

# load saved model
with open('ridge_model' , 'rb') as p:
new_reg = pickle.load(p)

r_squared = new_reg.score(x_test,y_test)
print(r-squared)


Here ridge is the regression model I created

df is the new data on which prediction needs to be done

y_test contains target variable x_test contains features in dataset except target variable

I want to understand:

1. 
   Can we pass entire new data set onto existing model for prediction and see how model is performing or not?
   
   
2. 
   r_squared = new_reg.score(x_test,y_test) in this line does .score calculates r-square as calculated on existing model or any other score it calculates?
   

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