pytorch-ts/pts/feature/lag.py

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

# Standard library imports
import re
from typing import List, Tuple, Optional

# Third-party imports
import numpy as np

# First-party imports
from .time_feature import (
    DayOfMonth,
    DayOfWeek,
    DayOfYear,
    HourOfDay,
    MinuteOfHour,
    MonthOfYear,
    TimeFeature,
    WeekOfYear,
)


def get_granularity(freq_str: str) -> Tuple[int, str]:
    """
    Splits a frequency string such as "7D" into the multiple 7 and the base
    granularity "D".

    Parameters
    ----------

    freq_str
        Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.
    """
    freq_regex = r"\s*((\d+)?)\s*([^\d]\w*)"
    m = re.match(freq_regex, freq_str)
    assert m is not None, "Cannot parse frequency string: %s" % freq_str
    groups = m.groups()
    multiple = int(groups[1]) if groups[1] is not None else 1
    granularity = groups[2]
    return multiple, granularity


def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
    """
    Returns a list of time features that will be appropriate for the given frequency string.

    Parameters
    ----------

    freq_str
        Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.

    """
    _, granularity = get_granularity(freq_str)
    if granularity == "M":
        feature_classes = [MonthOfYear]
    elif granularity == "W":
        feature_classes = [DayOfMonth, WeekOfYear]
    elif granularity in ["D", "B"]:
        feature_classes = [DayOfWeek, DayOfMonth, DayOfYear]
    elif granularity == "H":
        feature_classes = [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear]
    elif granularity in ["min", "T"]:
        feature_classes = [MinuteOfHour, HourOfDay, DayOfWeek, DayOfMonth, DayOfYear]
    else:
        supported_freq_msg = f"""
        Unsupported frequency {freq_str}

        The following frequencies are supported:

            M   - monthly
            W   - week
            D   - daily
            H   - hourly
            min - minutely
        """
        raise RuntimeError(supported_freq_msg)

    return [cls() for cls in feature_classes]


def _make_lags(middle: int, delta: int) -> np.ndarray:
    """
    Create a set of lags around a middle point including +/- delta
    """
    return np.arange(middle - delta, middle + delta + 1).tolist()


def get_lags_for_frequency(
    freq_str: str, lag_ub: int = 1200, num_lags: Optional[int] = None
) -> List[int]:
    """
    Generates a list of lags that that are appropriate for the given frequency string.

    By default all frequencies have the following lags: [1, 2, 3, 4, 5, 6, 7].
    Remaining lags correspond to the same `season` (+/- `delta`) in previous `k` cycles.
    Here `delta` and `k` are chosen according to the existing code.

    Parameters
    ----------

    freq_str
        Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.

    lag_ub
        The maximum value for a lag.

    num_lags
        Maximum number of lags; by default all generated lags are returned
    """

    multiple, granularity = get_granularity(freq_str)

    # Lags are target values at the same `season` (+/- delta) but in the previous cycle.
    def _make_lags_for_minute(multiple, num_cycles=3):
        # We use previous ``num_cycles`` hours to generate lags
        return [_make_lags(k * 60 // multiple, 2) for k in range(1, num_cycles + 1)]

    def _make_lags_for_hour(multiple, num_cycles=7):
        # We use previous ``num_cycles`` days to generate lags
        return [_make_lags(k * 24 // multiple, 1) for k in range(1, num_cycles + 1)]

    def _make_lags_for_day(multiple, num_cycles=4):
        # We use previous ``num_cycles`` weeks to generate lags
        # We use the last month (in addition to 4 weeks) to generate lag.
        return [_make_lags(k * 7 // multiple, 1) for k in range(1, num_cycles + 1)] + [
            _make_lags(30 // multiple, 1)
        ]

    def _make_lags_for_week(multiple, num_cycles=3):
        # We use previous ``num_cycles`` years to generate lags
        # Additionally, we use previous 4, 8, 12 weeks
        return [_make_lags(k * 52 // multiple, 1) for k in range(1, num_cycles + 1)] + [
            [4 // multiple, 8 // multiple, 12 // multiple]
        ]

    def _make_lags_for_month(multiple, num_cycles=3):
        # We use previous ``num_cycles`` years to generate lags
        return [_make_lags(k * 12 // multiple, 1) for k in range(1, num_cycles + 1)]

    if granularity == "M":
        lags = _make_lags_for_month(multiple)
    elif granularity == "W":
        lags = _make_lags_for_week(multiple)
    elif granularity == "D":
        lags = _make_lags_for_day(multiple) + _make_lags_for_week(multiple / 7.0)
    elif granularity == "B":
        # todo find good lags for business day
        lags = []
    elif granularity == "H":
        lags = (
            _make_lags_for_hour(multiple)
            + _make_lags_for_day(multiple / 24.0)
            + _make_lags_for_week(multiple / (24.0 * 7))
        )
    elif granularity == "min":
        lags = (
            _make_lags_for_minute(multiple)
            + _make_lags_for_hour(multiple / 60.0)
            + _make_lags_for_day(multiple / (60.0 * 24))
            + _make_lags_for_week(multiple / (60.0 * 24 * 7))
        )
    else:
        raise Exception("invalid frequency")

    # flatten lags list and filter
    lags = [int(lag) for sub_list in lags for lag in sub_list if 7 < lag <= lag_ub]
    lags = [1, 2, 3, 4, 5, 6, 7] + sorted(list(set(lags)))

    return lags[:num_lags]