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534 def load(*, path='./dataset3'): 535 dataset = list() 536 for file in os.listdir(path): 537 if os.path.splitext(file)[1] != '.json': 538 continue 539 with open(f'{path}/{file}', 'r') as load_file: 540 data = json.load(load_file, object_hook=object_hook) 541 for item in data: 542 dataset.append(Info(item)) 543 return Dataset(dataset)
92 def load_dataset(data_file=('%s/%s' % (DATA_DIR, DATA_FILE))): 93 94 dataset = utils_backdoor.load_dataset(data_file, keys=['X_test', 'Y_test']) 95 96 X_test = np.array(dataset['X_test'], dtype='float32') 97 Y_test = np.array(dataset['Y_test'], dtype='float32') 98 99 print('X_test shape %s' % str(X_test.shape)) 100 print('Y_test shape %s' % str(Y_test.shape)) 101 102 return X_test, Y_test
170 def load_dataset(): 171 global data_path 172 dataset = Dataset(data_path, verbose=True) 173 dataset_size = len(dataset.samples) 174 assert dataset_size > 0 175 return dataset
61 def load_pandas(): 62 data = load() 63 # pandas <= 0.12.0 fails in the to_datetime regex on Python 3 64 index = pd.DatetimeIndex(start=data.data['date'][0].decode('utf-8'), 65 periods=len(data.data), format='%Y%m%d', 66 freq='W-SAT') 67 dataset = pd.DataFrame(data.data['co2'], index=index, columns=['co2']) 68 #NOTE: this is how I got the missing values in co2.csv 69 #new_index = pd.DatetimeIndex(start='1958-3-29', end=index[-1], 70 # freq='W-SAT') 71 #data.data = dataset.reindex(new_index) 72 data.data = dataset 73 return data
35 def load_dataset(data_arg, wvpath, embedding_size): 36 wv = wordvec_class(wvpath) 37 dm = data_class(**data_arg) 38 return dm, wv.load_matrix(embedding_size, dm.vocab_list)
43 def load(self, *args, **kwargs): 44 """Load data and check the channel number `c_dim`. 45 """ 46 self._load(*args, **kwargs) 47 click.secho(" [*] Dataset '%s' loaded." % self.name, fg="green")
22 def get_dataset(): 23 dataset_filename = 'dataset/annoted_dataset.pkl' 24 if not os.path.isfile(dataset_filename): 25 annoted_data = read_json_formatted() 26 dataset = [] 27 for row in annoted_data: 28 sources = [s.lower() for s in row['target']] 29 targets = [s.lower() for s in row['polarity']] 30 sentence_meta = {} 31 sentence = row['sentence'] 32 for source, target in zip(sources, targets): 33 sentence_meta[source] = target 34 dataset.append({'sentence': sentence, 'meta': sentence_meta}) 35 pd.to_pickle(dataset, dataset_filename) 36 else: 37 dataset = pd.read_pickle(dataset_filename) 38 return dataset
581 def load_data(self,param,dates): 582 if param=='PM2.5': 583 df = self.load_aqs_pm25_data(dates) 584 elif param == 'PM10': 585 df = self.load_aqs_pm10_data(dates) 586 elif param == 'SPEC': 587 df = self.load_aqs_spec_data(dates) 588 elif param == 'CO': 589 df = self.load_aqs_co_data(dates) 590 elif param == 'OZONE': 591 df = self.load_aqs_ozone_data(dates) 592 elif param == 'SO2': 593 df = self.load_aqs_so2_data(dates) 594 elif param == 'VOC': 595 df = self.load_aqs_voc_data(dates) 596 elif param == 'NONOXNOY': 597 df = self.load_aqs_nonoxnoy_data(dates) 598 elif param == 'WIND': 599 df = self.load_aqs_wind_data(dates) 600 elif param == 'TEMP': 601 df = self.load_aqs_temp_data(dates) 602 elif param == 'RHDP': 603 df = self.load_aqs_rhdp_data(dates) 604 return df
66 def load(self): 67 self.open_file(self.path)
15 def load_data(dataset=""): 16 base_dir = os.path.join("data", dataset) 17 assert os.path.exists(base_dir), \ 18 "Could not find data directory: " + base_dir 19 20 model_path = os.path.join(base_dir, "model.pkl.gz") 21 model = None 22 if os.path.exists(model_path): 23 with gzip.open(model_path, "r") as f: 24 model = cPickle.load(f) 25 26 train_path = os.path.join(base_dir, "train.pkl.gz") 27 with gzip.open(train_path, "r") as f: 28 train = cPickle.load(f) 29 30 test_path = os.path.join(base_dir, "test.pkl.gz") 31 with gzip.open(test_path, "r") as f: 32 test = cPickle.load(f) 33 34 return train, test, model