Source code for

from typing import Dict, Tuple, List

import numpy as np
import pandas as pd
from type_infer.dtype import dtype

from lightwood.api.types import TimeseriesSettings
from lightwood.helpers.ts import get_ts_groups, get_delta, Differencer
from lightwood.encoder.time_series.helpers.common import generate_target_group_normalizers

[docs]def timeseries_analyzer(data: Dict[str, pd.DataFrame], dtype_dict: Dict[str, str], timeseries_settings: TimeseriesSettings, target: str) -> Dict: """ This module analyzes (pre-processed) time series data and stores a few useful insights used in the rest of Lightwood's pipeline. :param data: dictionary with the dataset split into train, val, test subsets. :param dtype_dict: dictionary with inferred types for every column. :param timeseries_settings: A `TimeseriesSettings` object. For more details, check `lightwood.types.TimeseriesSettings`. :param target: name of the target column. The following things are extracted from each time series inside the dataset: - group_combinations: all observed combinations of values for the set of `group_by` columns. The length of this list determines how many time series are in the data. - deltas: inferred sampling interval - ts_naive_residuals: Residuals obtained from the data by a naive forecaster that repeats the last-seen value. - ts_naive_mae: Mean residual value obtained from the data by a naive forecaster that repeats the last-seen value. - target_normalizers: objects that may normalize the data within any given time series for effective learning. See `lightwood.encoder.time_series.helpers.common` for available choices. :return: Dictionary with the aforementioned insights and the `TimeseriesSettings` object for future references. """ # noqa tss = timeseries_settings groups = get_ts_groups(data['train'], tss) deltas, periods, freqs = get_delta(data['train'], tss) normalizers = generate_target_group_normalizers(data['train'], target, dtype_dict, tss) if dtype_dict[target] in (dtype.integer, dtype.float, dtype.num_tsarray): naive_forecast_residuals, scale_factor = get_grouped_naive_residuals(data['dev'], target, tss) differencers = get_differencers(data['train'], target, tss.group_by) else: naive_forecast_residuals, scale_factor = {}, {} differencers = {} return {'target_normalizers': normalizers, 'deltas': deltas, 'tss': tss, 'group_combinations': groups, 'ts_naive_residuals': naive_forecast_residuals, 'ts_naive_mae': scale_factor, 'periods': periods, 'sample_freqs': freqs, 'stl_transforms': {}, # TODO: remove, or provide from outside as user perhaps 'differencers': differencers }
def get_naive_residuals(target_data: pd.DataFrame, m: int = 1) -> Tuple[List, float]: """ Computes forecasting residuals for the naive method (forecasts for time `t` is the value observed at `t-1`). Useful for computing MASE forecasting error. As per, we resort to a constant forecast based on the last-seen measurement across the entire horizon. By following the original measure, the naive forecaster would have the advantage of knowing the actual values whereas the predictor would not. Note: method assumes predictions are all for the same group combination. For a dataframe that contains multiple series, use `get_grouped_naive_resiudals`. :param target_data: observed time series targets :param m: season length. the naive forecasts will be the m-th previously seen value for each series :return: (list of naive residuals, average residual value) """ # noqa # @TODO: support categorical series as well residuals = np.abs(target_data.values[1:] - target_data.values[0]).flatten() scale_factor = np.average(residuals) return residuals.tolist(), scale_factor def get_grouped_naive_residuals( info: pd.DataFrame, target: str, tss: TimeseriesSettings ) -> Tuple[Dict, Dict]: """ Wraps `get_naive_residuals` for a dataframe with multiple co-existing time series. """ # noqa group_residuals = {} group_scale_factors = {} grouped = info.groupby(by=tss.group_by) if tss.group_by else info.groupby(lambda x: '__default') for group, subset in grouped: if subset.shape[0] > 1: residuals, scale_factor = get_naive_residuals(subset[target]) # @TODO: pass m once we handle seasonality group_residuals[group] = residuals group_scale_factors[group] = scale_factor return group_residuals, group_scale_factors def get_differencers(data: pd.DataFrame, target: str, group_cols: List): differencers = {} grouped = data.groupby(by=group_cols) if group_cols else data.groupby(lambda x: True) for group, subset in grouped: differencer = Differencer()[target].values) differencers[group] = differencer return differencers