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How to use get_raw_path method in localstack

Best Python code snippet using localstack_python

processor.py

Source:processor.py

...53        form more useful for classification such as one hot encoding.54        """55        pass56    @classmethod57    def get_raw_path(cls):58        return cls.RAW_DATA_PATHS[cls.NAME]59    @classmethod60    def get_path(cls):61        return cls.DATA_PATHS[cls.NAME]62class EcoliDataProcessor(AbstractDataProcessor):63    NAME = 'ecoli'64    @classmethod65    def convert_raw_data_to_df(cls) -> Tuple[str, DataFrame]:66        """Converts raw ecoli data to a dataframe"""67        # path to raw data file68        path = cls.get_raw_path()69        # names of columns in the dataframe70        names = ['sequence'] + [f'feature_{i}' for i in range(1, 8)] + ['label']71        # create dataframe72        df = pd.read_csv(path, delim_whitespace=True, names=names)73        return cls.NAME, df74class GlassDataProcessor(AbstractDataProcessor):75    NAME = 'glass'76    @classmethod77    def convert_raw_data_to_df(cls) -> Tuple[str, DataFrame]:78        """Converts raw glass data to a dataframe."""79        path = cls.get_raw_path()80        # name of columns in the dataframe81        names = ['index']+[f'feature_{i}' for i in range(9)]+['label']82        # TODO should we drop the index column83        # create dataframe84        df = pd.read_csv(path, names=names)85        return cls.NAME, df86class LetterDataProcessor(AbstractDataProcessor):87    NAME = 'letter'88    NUM_FEATURES = 1689    @classmethod90    def convert_raw_data_to_df(cls) -> Tuple[str, DataFrame]:91        """Converts raw letter data into a dataframe"""92        path = cls.get_raw_path()93        # names of columns94        names = ['label']+[f'feature_{i}' for i in range(cls.NUM_FEATURES)]95        df = pd.read_csv(path, names=names)96        return cls.NAME, df97class OptdigitsDataProcessor(AbstractDataProcessor):98    NAME = 'optdigits'99    NUM_FEATURES = 64100    @classmethod101    def convert_raw_data_to_df(cls) -> Tuple[str, DataFrame]:102        """converts raw optdigits data into dataframe"""103        path = cls.get_raw_path()104        # names of columns105        names = [f'feature_{i}' for i in range(cls.NUM_FEATURES)]+['label']106        df = pd.read_csv(path, names=names)107        return cls.NAME, df108class AbaloneDataProcessor(AbstractDataProcessor):109    NAME = 'abalone'110    NUM_FEATURES = 8111    @classmethod112    def convert_raw_data_to_df(cls) -> Tuple[str, DataFrame]:113        """converts raw optdigits data into dataframe"""114        path = cls.get_raw_path()115        # names of columns116        names = [f'feature_{i}' for i in range(cls.NUM_FEATURES)]+['label']117        df = pd.read_csv(path, names=names)118        return cls.NAME, df119# collect data processors in map for use my loaders120PROCESSOR_MAP = {121    'ecoli': EcoliDataProcessor,122    'glass': GlassDataProcessor,123    'letter': LetterDataProcessor,124    'optdigits': OptdigitsDataProcessor,125    'abalone': AbaloneDataProcessor...

run_preprocessing.py

Source:run_preprocessing.py

...5# Setup path helpers and file paths6get_raw_path = lambda file : f"./data/raw/{file}"7get_prep_path = lambda file: f"./data/preprocessed/{file}"8# TODO: Convert path data structure to dict9input_full_train_pos_path = get_raw_path("train_pos_full.txt")10input_full_train_neg_path = get_raw_path("train_neg_full.txt")11input_part_train_pos_path = get_raw_path("part_train_pos.txt")12input_part_train_neg_path = get_raw_path("part_train_neg.txt")13input_test_path = get_raw_path("test_data.txt")14output_full_train_pos_path = get_prep_path("train_pos_full.txt")15output_full_train_neg_path = get_prep_path("train_neg_full.txt")16output_part_train_pos_path = get_prep_path("part_train_pos.txt")17output_part_train_neg_path = get_prep_path("part_train_neg.txt")18output_test_path_noid = get_prep_path("test_prep_noid.txt")19output_test_path = get_prep_path("test_prep.txt")20def run_preprocessing(is_train=False, is_full=True):21  preprocessing = PipelineMMST()22  if is_train and is_full:23    preprocessing.process(24      [ input_full_train_pos_path, input_full_train_neg_path ],25      [ output_full_train_pos_path, output_full_train_neg_path ]26    )27  elif is_train and not is_full:...

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