How to use composeml - 10 common examples

def _check_parameters(self, size, start, step):
        """Checks if parameters are data slice offsets."""
        if not isinstance(size, DataSliceStep):
            size = DataSliceStep(size)

        start = start or self._df.index[0]
        if not isinstance(start, DataSliceOffset):
            start = DataSliceOffset(start)

        step = step or size
        if not isinstance(step, DataSliceStep):
            step = DataSliceStep(step)

        info = 'offset must be positive'
        assert size._is_positive, info
        assert step._is_positive, info

        if any(offset._is_offset_period for offset in (size, start, step)):
            info = 'offset by time requires a time index'
            assert self._is_time_index, info

def _check_parameters(self, size, start, step):
        """Checks if parameters are data slice offsets."""
        if not isinstance(size, DataSliceStep):
            size = DataSliceStep(size)

        start = start or self._df.index[0]
        if not isinstance(start, DataSliceOffset):
            start = DataSliceOffset(start)

        step = step or size
        if not isinstance(step, DataSliceStep):
            step = DataSliceStep(step)

        info = 'offset must be positive'
        assert size._is_positive, info
        assert step._is_positive, info

        if any(offset._is_offset_period for offset in (size, start, step)):
            info = 'offset by time requires a time index'
            assert self._is_time_index, info

        return size, start, step

def _check_parameters(self, size, start, step):
        """Checks if parameters are data slice offsets."""
        if not isinstance(size, DataSliceStep):
            size = DataSliceStep(size)

        start = start or self._df.index[0]
        if not isinstance(start, DataSliceOffset):
            start = DataSliceOffset(start)

        step = step or size
        if not isinstance(step, DataSliceStep):
            step = DataSliceStep(step)

        info = 'offset must be positive'
        assert size._is_positive, info
        assert step._is_positive, info

        if any(offset._is_offset_period for offset in (size, start, step)):
            info = 'offset by time requires a time index'
            assert self._is_time_index, info

        return size, start, step

def _check_parameters(self, size, start, step):
        """Checks if parameters are data slice offsets."""
        if not isinstance(size, DataSliceStep):
            size = DataSliceStep(size)

        start = start or self._df.index[0]
        if not isinstance(start, DataSliceOffset):
            start = DataSliceOffset(start)

        step = step or size
        if not isinstance(step, DataSliceStep):
            step = DataSliceStep(step)

        info = 'offset must be positive'
        assert size._is_positive, info
        assert step._is_positive, info

        if any(offset._is_offset_period for offset in (size, start, step)):
            info = 'offset by time requires a time index'

def read_parquet(path, filename='label_times.parquet', load_settings=True):
    """Read label times in parquet format from disk.

    Args:
        path (str) : Directory on disk to read from.
        filename (str) : Filename for label times. Default value is `label_times.parquet`.
        load_settings (bool) : Whether to load the settings used to make the label times.

    Returns:
        LabelTimes : Deserialized label times.
    """
    file = os.path.join(path, filename)
    assert os.path.exists(file), "data not found: '%s'" % file

    data = pd.read_parquet(file)
    label_times = LabelTimes(data=data)

    if load_settings:
        label_times = label_times._load_settings(path)

    return label_times

def read_pickle(path, filename='label_times.pickle', load_settings=True):
    """Read label times in parquet format from disk.

    Args:
        path (str) : Directory on disk to read from.
        filename (str) : Filename for label times. Default value is `label_times.parquet`.
        load_settings (bool) : Whether to load the settings used to make the label times.

    Returns:
        LabelTimes : Deserialized label times.
    """
    file = os.path.join(path, filename)
    assert os.path.exists(file), "data not found: '%s'" % file

    data = pd.read_pickle(file)
    label_times = LabelTimes(data=data)

    if load_settings:
        label_times = label_times._load_settings(path)

    return label_times

def read_csv(path, filename='label_times.csv', load_settings=True):
    """Read label times in csv format from disk.

    Args:
        path (str) : Directory on disk to read from.
        filename (str) : Filename for label times. Default value is `label_times.csv`.
        load_settings (bool) : Whether to load the settings used to make the label times.

    Returns:
        LabelTimes : Deserialized label times.
    """
    file = os.path.join(path, filename)
    assert os.path.exists(file), "data not found: '%s'" % file

    data = pd.read_csv(file, index_col='id')
    label_times = LabelTimes(data=data)

    if load_settings:
        label_times = label_times._load_settings(path)

    return label_times

def _constructor(self):
        return LabelTimes

is_label_search = isinstance(num_examples_per_instance, dict)
        search = (LabelSearch if is_label_search else ExampleSearch)(num_examples_per_instance)

        records = self._run_search(
            df=df,
            search=search,
            gap=gap,
            min_data=minimum_data,
            drop_empty=drop_empty,
            verbose=verbose,
            *args,
            **kwargs,
        )

        lt = LabelTimes(
            data=records,
            target_columns=list(self.labeling_function),
            target_entity=self.target_entity,
            search_settings={
                'num_examples_per_instance': num_examples_per_instance,
                'minimum_data': str(minimum_data),
                'window_size': str(self.window_size),
                'gap': str(gap),
            },
        )

        return lt

def _load_settings(self, path):
        """Read the settings in json format from disk.

        Args:
            path (str) : Directory on disk to read from.
        """
        file = os.path.join(path, 'settings.json')
        assert os.path.exists(file), 'settings not found'

        with open(file, 'r') as file:
            settings = json.load(file)

        if 'dtypes' in settings:
            dtypes = settings.pop('dtypes')
            self = LabelTimes(self.astype(dtypes))

        self.settings.update(settings)

        return self