TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'
numpy, python, python-2.7, scikit-learn, scipy
Solution
The `TypeError` problem stems from salaries being a list of strings while y_train_actual is a list of floats. Those cannot be subtracted.
For your second error, you should make sure that both arrays are of the same size, otherwise it cannot subtract them.
Problem
I am trying to calculate the Mean Squared Error of the predictions `y_train_actual` from my sci-kit learn model with the original values `salaries`. Problem: However with `mean_squared_error(y_train_actual, salaries)`, I am getting the error `TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'`. Using `list(salaries)` instead of `salaries` as the 2nd parameter gives the same error. With `mean_squared_error(y_train_actual, y_valid_actual)` I am getting the error `Found array with dim 40663. Expected 244768` How can I convert to the correct array types for `sklearn.netrucs.mean_squared_error()`? Code ``` from sklearn.metrics import mean_squared_error y_train_actual = [ np.exp(float(row)) for row in y_train ] print mean_squared_error(y_train_actual, salaries) ``` Error ``` TypeError Traceback (most recent call last) <ipython-input-144-b6d4557ba9c5> in <module>() 3 y_valid_actual = [ np.exp(float(row)) for row in y_valid ] 4 ----> 5 print mean_squared_error(y_train_actual, salaries) 6 print mean_squared_error(y_train_actual, y_valid_actual) C:\Python27\lib\site-packages\sklearn\metrics\metrics.pyc in mean_squared_error(y_true, y_pred) 1462 """ 1463 y_true, y_pred = check_arrays(y_true, y_pred) -> 1464 return np.mean((y_pred - y_true) ** 2) 1465 1466 TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray' ``` Code ``` y_train_actual = [ np.exp(float(row)) for row in y_train ] y_valid_actual = [ np.exp(float(row)) for row in y_valid ] print mean_squared_error(y_train_actual, y_valid_actual) ``` Error ``` ValueError Traceback (most recent call last) <ipython-input-146-7fcd0367c6f1> in <module>() 4 5 #print mean_squared_error(y_train_actual, salaries) ----> 6 print mean_squared_error(y_train_actual, y_valid_actual) C:\Python27\lib\site-packages\sklearn\metrics\metrics.pyc in mean_squared_error(y_true, y_pred) 1461 1462 """ -> 1463 y_true, y_pred = check_arrays(y_true, y_pred) 1464 return np.mean((y_pred - y_true) ** 2) 1465 C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in check_arrays(*arrays, **options) 191 if size != n_samples: 192 raise ValueError("Found array with dim %d. Expected %d" --> 193 % (size, n_samples)) 194 195 if not allow_lists or hasattr(array, "shape"): ValueError: Found array with dim 40663. Expected 244768 ``` Code ``` print type(y_train) print type(y_train_actual) print type(salaries) ``` Result ``` <type 'list'> <type 'list'> <type 'tuple'> ``` print y_train[:10] `[10.126631103850338, 10.308952660644293, 10.308952660644293, 10.221941283654663, 10.126631103850338, 10.126631103850338, 11.225243392518447, 9.9987977323404529, 10.043249494911286, 11.350406535472453]` print salaries[:10] `('25000', '30000', '30000', '27500', '25000', '25000', '75000', '22000', '23000', '85000')` print list(salaries)[:10] `['25000', '30000', '30000', '27500', '25000', '25000', '75000', '22000', '23000', '85000']` print len(y_train) ``` 244768 ``` print len(salaries) ``` 244768 ```