How to use _wrapper method in pandera

Best Python code snippet using pandera_python

Main2.py

Source:Main2.py

...74            f1_micro[algorithm + '_wrapper'] = np.zeros((9, 10))75            train_time[algorithm + '_wrapper'] = np.zeros((9, 10))76            test_time[algorithm + '_wrapper'] = np.zeros((9, 10))77            for percent in range(1, 10):78                a, b, c, d, e = wrapper.cross_validation_wrapper(79                    data_name=data_set_name, mlc_type=mlc, compare_with=algorithm, feature_threshold=(percent * 0.1),80                    need_normalize=need_normalize, need_sampling=need_sampling, need_contaminate=need_contaminate)81                hamming_loss[algorithm + '_wrapper'][percent-1, :], f1_macro[algorithm + '_wrapper'][percent-1, :],\82                f1_micro[algorithm + '_wrapper'][percent-1, :], train_time[algorithm + '_wrapper'][percent-1, :],\83                test_time[algorithm + '_wrapper'][percent-1, :] = a[percent-1, :], b[percent-1, :], c[percent-1, :],\84                                                                  d[percent-1, :], e[percent-1, :]85            np.save(dir_name + '/hamming_loss_' + algorithm + '_wrapper', hamming_loss[algorithm + '_wrapper'])86            np.save(dir_name + '/f1_macro_' + algorithm + '_wrapper', f1_macro[algorithm + '_wrapper'])87            np.save(dir_name + '/f1_micro_' + algorithm + '_wrapper', f1_micro[algorithm + '_wrapper'])88            np.save(dir_name + '/train_time_' + algorithm + '_wrapper', train_time[algorithm + '_wrapper'])89            np.save(dir_name + '/test_time_' + algorithm + '_wrapper', test_time[algorithm + '_wrapper'])90        else:91            ranking_loss[algorithm + '_wrapper'] = np.zeros((9, 10))92            average_precision[algorithm + '_wrapper'] = np.zeros((9, 10))93            train_time[algorithm + '_wrapper'] = np.zeros((9, 10))94            test_time[algorithm + '_wrapper'] = np.zeros((9, 10))95            for percent in range(1, 10):96                a, b, c, d = wrapper.cross_validation_wrapper(97                    data_name=data_set_name, mlc_type=mlc, compare_with=algorithm, feature_threshold=(percent * 0.1),98                    need_normalize=need_normalize, need_sampling=need_sampling, need_contaminate=need_contaminate)99                ranking_loss[algorithm + '_wrapper'][percent-1, :],\100                average_precision[algorithm + '_wrapper'][percent-1, :],\101                train_time[algorithm + '_wrapper'][percent-1, :],\102                test_time[algorithm + '_wrapper'][percent-1, :] = a[percent-1, :], b[percent-1, :], c[percent-1, :],\103                                                                  d[percent-1, :]104            np.save(dir_name + '/ranking_loss_' + algorithm + '_wrapper', ranking_loss[algorithm + '_wrapper'])105            np.save(dir_name + '/average_precision_' + algorithm + '_wrapper', average_precision[algorithm + '_wrapper'])106            np.save(dir_name + '/train_time_' + algorithm + '_wrapper', train_time[algorithm + '_wrapper'])107            np.save(dir_name + '/test_time_' + algorithm + '_wrapper', test_time[algorithm + '_wrapper'])108    return load_results(data_set_name, mlc, test_name, algorithm_list_filter, algorithm_list_wrapper)109    #  return hamming_loss, f1_macro, f1_micro, ranking_loss, average_precision, train_time, test_time110def load_results(data_set_name, mlc, test_name, algorithm_list_filter=[], algorithm_list_wrapper=[]):...

Main.py

Source:Main.py

...72            np.save(dir_name + '/test_time_' + algorithm, test_time[algorithm])73    for algorithm in algorithm_list_wrapper:74        if mlc != 'CLR':75            hamming_loss[algorithm + '_wrapper'], f1_macro[algorithm + '_wrapper'], f1_micro[algorithm + '_wrapper'],\76             train_time[algorithm + '_wrapper'], test_time[algorithm + '_wrapper'] = wrapper.cross_validation_wrapper(77                    data_name=data_set_name, mlc_type=mlc, compare_with=algorithm, feature_threshold=0,78                    need_normalize=need_normalize, need_sampling=need_sampling, need_contaminate=need_contaminate,79                    need_shuffling=need_shuffling)80            np.save(dir_name + '/hamming_loss_' + algorithm + '_wrapper', hamming_loss[algorithm + '_wrapper'])81            np.save(dir_name + '/f1_macro_' + algorithm + '_wrapper', f1_macro[algorithm + '_wrapper'])82            np.save(dir_name + '/f1_micro_' + algorithm + '_wrapper', f1_micro[algorithm + '_wrapper'])83            np.save(dir_name + '/train_time_' + algorithm + '_wrapper', train_time[algorithm + '_wrapper'])84            np.save(dir_name + '/test_time_' + algorithm + '_wrapper', test_time[algorithm + '_wrapper'])85        else:86            ranking_loss[algorithm + '_wrapper'], average_precision[algorithm + '_wrapper'], \87             train_time[algorithm + '_wrapper'], test_time[algorithm + '_wrapper'] =\88             wrapper.cross_validation_wrapper(89                    data_name=data_set_name, mlc_type=mlc, compare_with=algorithm, feature_threshold=0,90                    need_normalize=need_normalize, need_sampling=need_sampling, need_contaminate=need_contaminate,91                    need_shuffling=need_shuffling)92            np.save(dir_name + '/ranking_loss_' + algorithm + '_wrapper', ranking_loss[algorithm + '_wrapper'])93            np.save(dir_name + '/average_precision_' + algorithm + '_wrapper', average_precision[algorithm + '_wrapper'])94            np.save(dir_name + '/train_time_' + algorithm + '_wrapper', train_time[algorithm + '_wrapper'])95            np.save(dir_name + '/test_time_' + algorithm + '_wrapper', test_time[algorithm + '_wrapper'])96    return load_results(data_set_name, mlc, test_name, algorithm_list_filter, algorithm_list_wrapper )97    #  return hamming_loss, f1_macro, f1_micro, ranking_loss, average_precision, train_time, test_time98def load_results(data_set_name, mlc, test_name, algorithm_list_filter=[], algorithm_list_wrapper=[]):99    # loading results from test() and show the plot100    hamming_loss = dict()101    f1_macro = dict()102    f1_micro = dict()...

yaxmlplus.py

Source:yaxmlplus.py

...126            for image in images:127                etree.SubElement(offer, "image").text = image            128        etree.SubElement(offer, "description").text = self._wrapper.description()129        if not is_stead:130            self.unit_wrapper(etree, etree.SubElement(offer, "area"), self._wrapper.area())131            if self._wrapper.living_space():132                self.unit_wrapper(etree, etree.SubElement(offer, "living-space"), self._wrapper.living_space())133            if self._wrapper.kitchen_space():134                self.unit_wrapper(etree, etree.SubElement(offer, "kitchen-space"), self._wrapper.kitchen_space())135            for room_space in self._wrapper.rooms_space():136                self.unit_wrapper(etree, etree.SubElement(offer, "room-space"), room_space)137            if self._wrapper.rooms_type():138                etree.SubElement(offer, "rooms-type").text = self._wrapper.rooms_type()            139            self.add_bool_element(etree, offer, 'kitchen-furniture', self._wrapper.kitchen_furniture())140            self.add_bool_element(etree, offer, 'room-furniture', self._wrapper.room_furniture())141            self.add_bool_element(etree, offer, 'television', self._wrapper.television())142            self.add_bool_element(etree, offer, 'washing-machine', self._wrapper.washing_machine())143            self.add_bool_element(etree, offer, 'refrigerator', self._wrapper.refrigerator())144            self.add_bool_element(etree, offer, 'alarm', self._wrapper.alarm())145        else:146            etree.SubElement(offer, "lot-type").text = self._wrapper.lot_type()            147        if has_stead: 148            self.unit_wrapper(etree, etree.SubElement(offer, "lot-area"), self._wrapper.lot_area(), u'ÑÐ¾Ñ')149        self.add_bool_element(etree, offer, 'new-flat', self._wrapper.new_flat())     150        if self._wrapper.rooms():151            etree.SubElement(offer, "rooms").text = self._wrapper.rooms()        152        if self._wrapper.rooms_offered():153            etree.SubElement(offer, "rooms-offered").text = self._wrapper.rooms_offered()154        155        if self._wrapper.is_studio():156            self.add_bool_element(etree, offer, 'open-plan', self._wrapper.is_studio())157                        158        self.add_bool_element(etree, offer, 'phone', self._wrapper.phone())        159        self.add_bool_element(etree, offer, 'internet', self._wrapper.internet())160        self.add_bool_element(etree, offer, 'mortgage', self._wrapper.mortgage())161        162        if self._wrapper.renovation():...

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