How to use _wrapper method in pandera

Best Python code snippet using pandera_python

Main2.py

Source: Main2.py Github

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...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=[]):...

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Main.py

Source: Main.py Github

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...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()...

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yaxmlplus.py

Source: yaxmlplus.py Github

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...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'сот')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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