def mse(grid_pred, real_events):
""" Computes the "mean aquared error" between the prediction and the ground
truth. Is computed as the sum of each cell squared difference.
:param grid_pred: An instance of :class:`GridPrediction` matrix attribute
must be normalized.
:param real_events: An instance of :class: open_cp.data.TimedPoints
:return: A non-negative floating point value
"""
grid_pred._matrix = ProcessData.normalize_matrix(grid_pred._matrix)
counting_matrix = make_counting_grid(grid_pred, real_events)
counting_matrix._matrix = ProcessData.normalize_matrix(
counting_matrix._matrix)
return mean_squared_error(grid_pred._matrix, counting_matrix._matrix)
def test_mse_match(self):
""" Test mse results match using different methods"""
infile = open(
'/Users/anamaria/Desktop/dev/security_project/aggressive_behavior_model/pkl/experiment_seppexp_10_2_siedco_prediction.pkl',
'rb')
loaded_siedco = pickle.load(infile)
infile.close()
grid = loaded_siedco['prediction'].values[0]
grid._matrix = ProcessData.normalize_matrix(grid._matrix)
real_events = loaded_siedco['eval_pts'].values[0]
mse_method_1 = prediction_metrics.mse(grid, real_events)
counting_matrix = prediction_metrics.make_counting_grid(
grid, real_events)
counting_matrix._matrix = ProcessData.normalize_matrix(
counting_matrix._matrix)
mse_method_2 = np.sum((grid._matrix.astype("float") -
counting_matrix._matrix.astype("float"))**2)
mse_method_2 /= float(grid._matrix.shape[0] * grid._matrix.shape[1])
self.assertEqual(mse_method_1, mse_method_2)
def test_normalize_matrix(self):
matrix = np.arange(1, 5)
matrix = matrix.reshape(2, 2)
matrix_expected = np.array([[0.25, 0.5], [0.75, 1]])
matrix_normalized = ProcessData.normalize_matrix(matrix)
self.assertTrue((matrix_normalized == matrix_expected).all())