most_common(1)[0][0]
training_testing_data.loc[:, "guess"] = most_frequent_training_class
new_col_names = {}
for column in training_testing_data.columns.values:
if type(column) != str:
new_col_names[column] = str(column).zfill(10)
training_testing_data.rename(columns=new_col_names, inplace=True)
// Transform the tree expression in a callable function
func = self._toolbox.compile(expr=self._optimized_pipeline)
After Change
"Please call fit() first."))
training_data = np.insert(self._training_features, 0, self._training_classes, axis=1) // Insert the classes
training_data = np.insert(training_data, 0, np.zeros((training_data.shape[0],)), axis=1) // Insert the group
testing_data = np.insert(testing_features, 0, np.zeros((testing_features.shape[0],)), axis=1) // Insert the classes
testing_data = np.insert(testing_data, 0, np.ones((testing_data.shape[0],)), axis=1) // Insert the group
most_frequent_class = Counter(self._training_classes).most_common(1)[0][0]
data = np.concatenate([training_data, testing_data])
data = np.insert(data, 0, np.array([most_frequent_class] * data.shape[0]), axis=1)
// Transform the tree expression in a callable function
func = self._toolbox.compile(expr=self._optimized_pipeline)
